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2024 IEEE International Conference on Imaging Systems and Techniques, IST 2024

作者： Jing, Xiuhan Zhang, Chenghong Zhang, He Lyu, Wenhao Fu, Bo Xu, Lijun School of Instrumentation and Optoelectronic Engineering Beihang University Key Laboratory of Precision Opto-Mechatronics Technology of Education Ministry Beijing China School of Engineering Medicine Beihang University Key Laboratory of Big Data-Based Precision Medicine Ministry of Industry and Information Technology Beijing China

ISBN: (纸本)9798350378214

Dual-comb spectroscopy is a competitive means of laser absorption spectroscopy owing to broad spectral range and high spectral resolution. Single-cavity dual-comb systems based on mode-locked lasers hold promising potential for further simplifying dual-comb spectroscopy. Two combs possess natural coherence because of common mode noise suppression. Herein, we proposed an all-fiber dispersion-managed dual-comb laser to broaden the spectra of dual combs. The output laser with a 3-dB bandwidth as broad as 9 nm at 1.5 μm was experimentally obtained. Dual combs were realized by the walk-off of orthogonal polarization modes and directly employed to absorption spec-troscopy due to their overlapped spectra. In order to improve the signal-to-noise ratio of dual-comb measurement, interferogram averaging was conducted, where the signal-to-noise ratio was generally proportional to the square root of averaging times. Besides, phase and frequency jitter was corrected by cross-correlation method. High resolution absorption lines of hydrogen cyanide from P18 to P26 were measured. Positions of measured absorption lines were in line with ones in HITRAN database, with an average error of approximate 1 pm. To the best of our knowledge, the 9-line result was the broadest bandwidth of polarization-multiplexed single-cavity dual combs without additional spectral spreading operation. The results demonstrate that the compact dual-comb spectrometer can be considered a potential candidate for quantitative measurements in practical applications. © 2024 IEEE.

关键词： Spectral resolution

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Broadband Optical Coupler based on the Helical Long-Period Gratings Inscribed in Seven-Core Fibers

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Journal of Lightwave Technology 2025年第12期43卷 5820-5828页

作者： Chen, Siyu Jiang, Chen Ma, Yuehui Zhao, Yunhe Fan, Xiaolong Mou, Chengbo Liu, Yunqi Shanghai University Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai Institute for Advanced Communication and Data Science School of Communication and Information Engineering Shanghai 200444 China Advanced Photonic Technology Lab Nanjing210023 China Shanghai Maritime University Institute of Logistics Science and Engineering Shanghai201306 China

We propose a broadband optical coupler based on cladding mode coupling of identical helical long-period gratings (HLPGs) inscribed in different cores of seven-core fibers (SCFs). The influence of the surrounding refractive index and offset distance between the input grating and tapping grating on the coupling efficiency of the coupler was investigated numerically and experimentally. A total peak coupling efficiency of 75.7% was achieved experimentally under the conditions of a surrounding refractive index of 1.454 and an offset distance of 90 mm when the light is launched into the center core of the SCF. The proposed optical coupler could have promising applications in multi-core fiber amplifiers and space-division multiplexing optical communications. © 1983-2012 IEEE.

关键词： Space division multiple access

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Inductive Relation Prediction with Logical Reasoning Using Contrastive Representations

Inductive Relation Prediction with Logical Reasoning Using C...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Pan, Yudai Liu, Jun Zhang, Lingling Zhao, Tianzhe Lin, Qika Hu, Xin Wang, Qianying School of Computer Science and Technology Xi'an Jiaotong University China National Engineering Lab for Big Data Analytics China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering China Lenovo Research Beijing China

Relation prediction in knowledge graphs (KGs) aims at predicting missing relations in incomplete triples, whereas the dominant embedding paradigm has a restriction on handling unseen entities during testing. In the real-world scenario, the inductive setting is more common because entities in the training process are finite. Previous methods capture an inductive ability by implicit logic in KGs. However, it would be challenging to preciously acquire entity-independent relational semantics of compositional logic rules and to deal with the deficient supervision of logic caused by the scarcity of relational semantics. To this end, we propose a novel graph convolutional network (GCN)-based model LogCo with logical reasoning by contrastive representations. LogCo firstly extracts enclosing subgraphs and relational paths between two entities to supply the entity-independence. Then a contrastive strategy for relational path instances and the subgraph is proposed for the issue of deficient supervision. The contrastive representations are learned for a joint training regime. Finally, prediction results and logic rules for reasoning are attained. Comprehensive experiments on twelve inductive datasets show that LogCo achieves outstanding performance comparing with SOTA inductive baselines. © 2022 Association for Computational Linguistics.

关键词： Forecasting

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Theoretical analyses of solute concentration-gradient driven water flow and solute concentration variation in solution-saturated semi-permeable materials

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Science China Technological Sciences 2025年第5期68卷 247-257页

作者： Yao LIU Chongbin ZHAO Bruce HOBBS Alison ORD Xiangtao ZHANG School of Tourism Data Guilin Tourism University Guangxi Key Laboratory of Culture and Tourism Smart Technology Guilin Tourism University Computational Geosciences Research Centre Central South University School of Earth and Environment the University of Western Australia Department of Hydraulic Engineering Tsinghua University

Based on the commonly-used definition of the semi-permeable material(SPM), in which the solvent(such as water)can pass through but the solute cannot move freely, a novel constitutive equation is presented, in this paper, to theoretically express the intrinsic constitutive relationship between the porosity variation rate with time and the solute concentration variation rate with time. From this theoretical finding, a mathematical model is established to describe the solute concentrationgradient driven water flow and solute concentration variation problem in solution-saturated semi-permeable materials(SSSPMs). In particular, the solute concentration variation in the considered problem can be mathematically described as a linear homogeneous second-order partial differential equation with a variable coefficient in the front of the time term. A special mathematical transform is presented and used to solve this equation, so that the analytical solution for the solute concentration variation in the considered problem has been derived in a purely mathematical manner. The derived analytical solution is then used to provide some theoretical understanding of solute concentration variations in the SSSPM layer.

关键词： water flow concentration gradient semi-permeable material theoretical analyses analytical solution mathematical proof

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Personalized Federated Learning with Feature Alignment via knowledge Distillation 21st

Personalized Federated Learning with Feature Alignment via...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Qi, Guangfei Qu, Zhihao Lyu, Shen-Huan Jia, Ninghui Ye, Baoliu Key Laboratory of Water Big Data Technology of Ministry of Water Resources College of Computer Science and Software Engineering Hohai University Nanjing China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9789819601189

Personalized Federated Learning (PFL) has gained significant attention for its ability to handle heterogeneous data effectively. Parameter decoupling is a typical approach to PFL. It decouples the model into a feature extractor and a classifier head, where the feature extractor is trained collaboratively to learn a common representation and the classifier head is personalized for local data. Since local training only learns personalized feature information and ignores global information, the generalization ability of the feature extractor is limited. To improve the performance of the local model, a feasible approach is to make the local feature extractor more generalized. However, prior work requires the transmission of additional feature data beyond the transmission of model parameters, which leads to privacy leakage and higher communication overhead. To address these shortcomings, we propose a PFL algorithm with feature alignment via knowledge distillation, named PFAKD. PFAKD enhances the training of local feature extractors by explicitly aligning each sample’s local features with global features, providing more detailed guidance. Meanwhile, it avoids additional communication overhead and the risk of privacy leakage. We conduct extensive experiments in heterogeneous data scenarios. PFAKD outperforms other state-of-the-art methods by up to 4.35% in terms of model accuracy. Our code is available at https://***/fei0829/PFAKD. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Head-wise Shareable Attention for Large Language Models

Head-wise Shareable Attention for Large Language Models

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Cao, Zouying Yang, Yifei Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China

ISBN: (纸本)9798891761681

Large Language Models (LLMs) suffer from huge number of parameters, which restricts their deployment on edge devices. Weight sharing is one promising solution that encourages weight reuse, effectively reducing memory usage with less performance drop. However, current weight sharing techniques primarily focus on small-scale models like BERT and employ coarse-grained sharing rules, e.g., layer-wise. This becomes limiting given the prevalence of LLMs and sharing an entire layer or block obviously diminishes the flexibility of weight sharing. In this paper, we present a perspective on head-wise shareable attention for large language models. We further propose two memory-efficient methods that share parameters across attention heads, with a specific focus on LLMs. Both of them use the same dynamic strategy to select the shared weight matrices. The first method directly reuses the pre-trained weights without retraining, denoted as DirectShare. The second method first post-trains with constraint on weight matrix similarity and then shares, denoted as PostShare. Experimental results reveal our head-wise shared models still maintain satisfactory capabilities, demonstrating the feasibility of fine-grained weight sharing applied to LLMs. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Research on Collaborative Storage of data Based on IPFS and Blockchain 7

Research on Collaborative Storage of Data Based on IPFS and ...

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7th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2024

作者： Han, Boyuan Liang, Zhihong Huang, Yuxiang Qin, Mingming Xiong, Fei School of Big Data and Intelligent Engineering Southwest Forestry University Kunming China Southwest Forestry University Key Laboratory of National Forestry and Grassland Administration on Forestry and Ecological Big Data China

ISBN: (纸本)9798350370782

The advent of the digital economy has necessitated secure and efficient storage solutions for large-scale data. Blockchain technology, characterized by its decentralization and immutability, offers unique advantages for protecting data privacy. However, as the scale of on-chain data processing expands, blockchain faces significant challenges in terms of storage costs and efficiency. This research introduces the IPFS-BC system, a novel collaborative storage solution that integrates the InterPlanetary File System (IPFS) with blockchain. This system harnesses IPFS' s capability to manage large-scale data and employs blockchain smart contracts to facilitate automated interactions between the IPFS and blockchain systems, thereby ensuring data integrity and security. The IPFS-BC system combines the blockchain's transaction record layer with IPFS's storage solution to achieve decentralized data storage and enhanced efficiency. Experimental validation demonstrates that this system effectively reduces the storage load on the blockchain, improves storage performance, and maintains data security and integrity. This study not only provides a new solution for large-scale data storage but also lays a practical foundation for further integration and interaction between blockchain and IPFS. © 2024 IEEE.

关键词： Storage efficiency

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Luojia-HSSR:A high spatial-spectral resolution remote sensing dataset for land-cover classification with a new 3D-HRNet

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Geo-Spatial Information Science 2023年第3期26卷 289-301页

作者： Yue Xu Jianya Gong Xin Huang Xiangyun Hu Jiayi Li Qiang Li Min Peng School of Remote Sensing and Information Engineering Wuhan UniversityWuhanChina State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanChina Hubei Luojia Laboratory WuhanChina Institute of Artificial Intelligence in Geomatics Wuhan UniversityWuhanChina Data Center Shenyang Surveying and Mapping Institute Co.LtdShenyangChina

High Spatial and Spectral Resolution(HSSR)remote-sensing images can provide rich spectral bands and detailed ground information,but there is a relative lack of research on this new type of remote-sensing *** there are already some HSSR datasets for deep learning model training and testing,the data volume of these datasets is small,resulting in low classification accuracy and weak generalization ability of the trained *** this paper,an HSSR dataset Luojia-HSSR is constructed based on aerial hyperspectral imagery of southern Shenyang City of Liaoning Province in *** our knowledge,it is the largest HSSR dataset to date,with 6438 pairs of 256×256 sized samples(including 3480 pairs in the training set,2209 pairs in the test set,and 749 pairs in the validation set),covering area of 161 km2 with spatial resolution 0.75 m,249 Visible and Near-Infrared(VNIR)spectral bands,and corresponding to 23 classes of field-validated ground *** is an ideal experimental data for spatial-spectral feature ***,a new deep learning model 3D-HRNet for interpreting HSSR images is *** conv-neck in HRNet is modified to better mine the spatial information of the ***,a 3D convolution module with attention mechanism is designed to capture the global-local fine spectral information ***,the 3D convolution is inserted into the HRNet to optimize the *** experiments show that the 3D-HRNet model has good interpreting ability for the Luojia-HSSR dataset with the Frequency Weighted Intersection over Union(FWIoU)reaching 80.54%,indicating that the Luojia-HSSR dataset constructed in this paper and the proposed 3D-HRnet model have good applicable prospects for processing HSSR remote sensing images.

关键词： High Spatial and Spectral Resolution(HSSR) remotesensing image classification deep learning Convolutional Neural Network(CNN)

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Differentially Private Federated Stochastic Primal-Dual Learning for Internet of Vehicles

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IEEE Internet of Things Journal 2025年第11期12卷 17034-17050页

作者： Li, Yiwei Wang, Shuai Chang, Tsung-Hui Xiamen University of Technology Fujian Key Laboratory of Communication Network and Information Processing Xiamen361024 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China The Chinese University of Hong Kong Shenzhen School of Science and Engineering Shenzhen Research Institute of Big Data Shenzhen518172 China

Federated learning (FL) has the potential to empower Internet-of-Vehicles (IoV) networks by enabling smart vehicles (SVs) to participate in the learning process under the orchestration of a vehicular service provider while keeping data locally. In this paper, we propose a novel federated stochastic primal-dual algorithm with differential privacy (FedSPD-DP) to ensure robust privacy protection for FL based IoV (FL-IoV) systems. The FedSPD-DP algorithm leverages multiple steps of local stochastic gradient descent (SGD) and partial client participation (PCP) to improve communication efficiency while incorporating differential privacy (DP) to ensure privacy protection. Our theoretical analysis explores the impact of these strategies on learning performance. Specifically, we demonstrate that the data sampling strategy and PCP enhance data privacy, while a larger number of local SGD steps may increase privacy leakage, revealing a non-trivial tradeoff between communication efficiency and privacy protection. Extensive experiments on real-world data validate the effectiveness of the proposed algorithm, showing superior performance compared to state-of-the-art FL algorithms, and confirming the analytical results and properties. © 2014 IEEE.

关键词： Federated learning

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A Novel Thermal Analysis Method for Tubular PM Linear Motors Based on Transfer Learning

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7379-7388页

作者： Wu, Tao Dai, Peipei Xue, Gang Guo, Youguang Lei, Gang Zhu, Jianguo Wang, Yifei China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China University of Technology Sydney School of Electrical and Data Engineering NSW2007 Australia University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia

Thermal modeling and analysis are critical for permanent magnet linear synchronous motors (PMLSMs), particularly in multi-physical analysis and motor design optimization. This paper proposes a new method for thermal steady state modeling and analysis based on transfer learning, which combines the thermal mechanism model with the data-driven model to achieve high-precision temperature analysis with small number of finite element analysis (FEA) samples. First, an equivalent thermal circuit (ETC) model is established for PMLSMs based on the structural parameters and heat transfer principles. Second, a hybrid model based on transfer learning is developed: the model is pre-trained by a large sample set generated from the ETC in the source field (mechanism analytical model), and then the trained model is transferred to the target field (FEA data-driven model). The parameters of the neural network in the target field are optimized by a few FEA sample sets with higher precision to improve the prediction accuracy of the target model. Experimental results demonstrate that the proposed method significantly boosts the performance of the deep neural network (DNN) model, particularly when training sets are small. The root mean square error (RMSE) of the DNN model decreases by 0.901 in the 5:5 (training/testing) dataset and 1.726 in the 2:8 dataset;and the corresponding R2 and maximal absolute error of motor performances are significantly decreased. The smaller the proportion of the training set, the better performance of the hybrid modeling will achieve. Furthermore, a temperature rise experiment conducted on a prototype motor validates the efficacy and precision of the proposed hybrid modeling methodology. © 2015 IEEE.

关键词： Permanent magnets

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