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Insight into the effect of gel drying temperature on the structure and desulfurization performance of ZnO/SiO_(2)adsorbents

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Chinese Journal of Chemical Engineering 2023年第4期56卷 233-241页

作者： Chao Yang Zhelin Su Yeshuang Wang Huiling Fan Meisheng Liang Zhaohui Chen College of Environmental Science and Engineering Taiyuan University of TechnologyTaiyuan 030024China State Key Laboratory of Clean and Efficient Coal Utilization Taiyuan University of TechnologyTaiyuan 030024China State Key Laboratory of Multi-phase Complex Systems Institute of Process EngineeringChinese Academy of SciencesBeijing 100190China

A series of ZnO/SiO_(2) adsorbents were prepared by a sol-gel method using tetraethyl orthosilicate,ethylene glycol(EG)and nitrates as *** effect of gel drying temperature on the structure and desulfurization performance of the adsorbents were investigated in *** is found that the low drying temperature led to a weak interaction among EG,Si AOH/H_(2)O and the nitrates in the gel system,which caused the oxidation of EG by NO3-and formed zinc glyoxylate complex during the gel calcination process,whereas this oxidation process also occurred at a high drying temperature during the gel drying *** formed zinc glyoxylate complex promoted the generation of monodentate carbonate on the surface of Zn O,which resulted in the inferior desulfurization performance of adsorbents despite they have smaller Zn O *** gel dried at 120°C formed the hydrogen bonds between EG and Si AOH/H_(2)O and a strong interaction between zinc oxo-clusters and NO3-was also found in the gel system,which avoided the oxidation of EG by NO3-during the preparation process and the Zn O nanoparticles with sizes of 6 nm were formed by a combustion *** adsorbent affords a highest sulfur capacity of 104.9 mg·g^(-1) in this *** addition,the gel drying temperature has a significant influence on the textural properties of the adsorbents except their surface area.

关键词： Sol-gel method Drying temperature H_(2)S removal Zinc-silica composites Hydrogen bonds

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MMH-FE:AMulti-Precision and Multi-Sourced Heterogeneous Privacy-Preserving Neural Network Training Based on Functional Encryption

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Computers, Materials & Continua 2025年第3期82卷 5387-5405页

作者： Hao Li Kuan Shao Xin Wang Mufeng Wang Zhenyong Zhang The State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science CenterQilu University of Technology(Shandong Academy of Sciences)Jinan250014China China Industrial Control Systems Cyber Emergency Response Team Beijing100040China

Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model ***,dishonest clouds may infer user data,resulting in user data *** schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing *** address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training ***,we design a multi-precision functional encryption computation based on Euclidean ***,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced ***,we conduct experiments on three *** results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.

关键词： Functional encryption multi-sourced heterogeneous data privacy preservation neural networks

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Can ChatGPT Boost Artistic Creation:The Need of Imaginative Intelligence for Parallel Art

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 835-838页

作者： Chao Guo Yue Lu Yong Dou Fei-Yue Wang The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

CHATGPT,one of the leading Large Language Models(LLMs),has acquired linguistic capabilities such as text comprehension and logical reasoning,enabling it to engage in natural conversations with humans.

关键词： such has reasoning

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Asymptotic properties of generalized maximum likelihood hyper-parameter estimator for regularized system identification 63

Asymptotic properties of generalized maximum likelihood hype...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Zhang, Meng Chen, Tianshi Mu, Biqiang The Chinese University of Hong Kong School of Data Science Shenzhen Research Institute of Big Data Shenzhen518172 China Institute of Systems Science Academy of Mathematics and System Science Chinese Academy of Sciences Key Laboratory of Systems and Control Beijing100190 China

ISBN: (纸本)9798350316339

Regularized system identification is one of the major advances in the field of system identification in the last decade. One key issue is the hyper-parameter estimation, for which the generalized maximum likelihood (GML) estimator is a popular one closely related to the empirical Bayes (EB) method. Considering the rich theoretical results on the EB estimator, the asymptotic properties of the GML estimator has not been studied before and is critical for understanding its efficacy when the sample size is large. In this paper, we investigate the asymptotic properties of the GML estimator and show that the GML estimator is asymptotically equivalent to the EB estimator. Furthermore, Monte-Carlo simulations verify their asymptotic equivalence and also indicates the GML estimator outperform the EB estimator for small sample sizes. © 2024 IEEE.

关键词： Decentralized systems

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Multi-channel generation of vortex beams with controllable polarization states and orbital angular momentum

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Chinese Physics B 2023年第12期32卷 90-97页

作者：吕子瑶王潘王长顺 State Key Laboratory of Advanced Optical Communication Systems and Networks School of Physics and AstronomyShanghai Jiao Tong UniversityShanghai 200240China School of Data Science The Chinese University of Hong KongShenzhen 518172China

Optical vortices with tunable polarization states and topological charges are widely investigated in various physical systems and practical devices for high-capacity optical ***,this kind of structured light beams is usually generated using several polarization and spatial phase devices,which decreases the configurability of optical ***,we have designed a kind of polarized optical multi-vortices generator based on the Stokes-Mueller formalism and cross-phase *** our scheme,multi-channel generation of polarized vortex beams can be realized through a single optical element and a single-input Gaussian *** polarization states and orbital angular momentum of the generated light beams are all-optically ***,the proposed polarized optical multi-vortices generator has also been demonstrated experimentally through one-step holographic recording in an azobenzene liquid-crystalline film and the experimental results agree with theoretical analysis.

关键词： light field modulation holography optical vortex polarization

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A Hyperparameter Optimization Method Based on Statistical Orthogonal Design for Neural Network Models 27th

A Hyperparameter Optimization Method Based on Statistical Or...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Wang, Yu Du, Bo Wu, Shufan Yang, Xingli School of Modern Educational Technology Shanxi University Taiyuan030006 China School of Mathematics and Statistics Shanxi University Taiyuan030006 China Key Laboratory of Complex Systems and Data Science Shanxi University Ministry of Education Taiyuan030006 China

ISBN: (纸本)9783031783944

In neural network models, hyperparameters have a significant impact on model performance. Currently, the commonly used hyperparameter optimization methods include manual search, grid search, random search, Bayesian optimization, and so on. However, these methods always exhibit some problems such as high computational cost, low convergence rate and poor model performance. Thus, for image classification tasks, a hyperparameter optimization method based on statistical orthogonal design for neural network models is proposed in this paper. With the same number of experiments, the classification accuracy of the proposed method is significantly better than that of grid search, random search, and Bayesian optimization methods for both two-level and three-level orthogonal designs. With the same classification accuracy as grid search, random search, and Bayesian optimization methods, the proposed method has fewer experimental times. Furthermore, the single-factor rotation method and statistical variance analysis technique are also applied to study the effect of different hyperparameters on the performances of the neural network models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neural network models

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Synergy between CSST galaxy survey and gravitational-wave observation: Inferring the Hubble constant from dark standard sirens

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science China(Physics,Mechanics & Astronomy) 2024年第3期67卷 7-19页

作者： Ji-Yu Song Ling-Feng Wang Yichao Li Ze-Wei Zhao Jing-Fei Zhang Wen Zhao Xin Zhang Key Laboratory of Cosmology and Astrophysics(Liaoning)&College of Sciences Northeastern UniversityShenyang 110819China CAS Key Laboratory for Researches in Galaxies and Cosmology Department of AstronomyUniversity of Science and Technology of ChinaChinese Academy of SciencesHefei 230026China School of Astronomy and Space Sciences University of Science and Technology of ChinaHefei 230026China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern UniversityShenyang 110819China Key Laboratory of Data Analytics and Optimization for Smart Industry(Ministry of Education) Northeastern Universty Shenyang 110819China

Gravitational waves(GWs) from compact binary coalescences encode the absolute luminosity distances of GW sources. Once the redshifts of GW sources are known, one can use the distance-redshift relation to constrain cosmological parameters. One way to obtain the redshifts is to localize GW sources by GW observations and then use galaxy catalogs to determine redshifts from a statistical analysis of redshift information of the potential host galaxies, commonly referred to as the dark siren method. The third-generation(3G) GW detectors are planned to work in the 2030s and will observe numerous compact binary *** these GW events as dark sirens requires high-quality galaxy catalogs from future sky survey projects. The China Space Station Telescope(CSST) will be launched in 2024 and will observe billions of galaxies within a 17500 deg^(2) survey area with redshift up to z ~ 4, providing photometric and spectroscopic galaxy catalogs. In this work, we simulate the CSST galaxy catalogs and the 5-year GW data from the 3G GW detectors and combine them to infer the Hubble constant(H_(0)). Our results show that the measurement precision of H0could reach the sub-percent level, meeting the standard of precision cosmology. We conclude that the synergy between CSST and the 3G GW detectors is of great significance in measuring the Hubble constant.

关键词： CSST galaxy survey galaxy catalogs gravitational waves dark sirens the Hubble constant

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Decomposing Visual and Semantic Correlations for Both Fully Supervised and Few-Shot Image Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1658-1668页

作者： Zhang, Chunjie Zheng, Xiaolong Beijing Jiaotong University Institute of Information Science Beijing100044 China Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China University of Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems The State of Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence Beijing100190 China

Most image classification methods are designed to either boost the classification accuracies with abundant supervision, or cope with the shortage of supervision information. This is often achieved by using the visual and semantic information of other sources. However, these methods use the visual and semantic information within the same class independently, leaving their intrinsic correlations unconsidered. Objects and disturbing components as well as noise exist on the same image. Besides, semantic representations also contain noisy information. To solve the problems mentioned above, we propose a novel method for both fully supervised image classification and few-shot image classification by decomposing visual and semantic correlations. We jointly explore the intrinsic correlations of visual and semantic information of images within the same class. For each class, we decompose its visual and semantic correlations using low-rank and sparse constraint respectively. The decomposed low-rank parts character the intrinsic correlations of images can be used in a linear transformation way. Using the decomposed parts of each class, Classification can be achieved by reconstruction error minimization. We conduct experiments on several datasets for both fully supervised image classification and few-shot image classification. Experimental results well show the effectiveness of the proposed method. © 2020 IEEE.

关键词： Semantics

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Kernel-Based Regularized Continuous-Time System Identification from Sampled data 63

Kernel-Based Regularized Continuous-Time System Identificati...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Fang, Xiaozhu Mu, Biqiang Chen, Tianshi The Chinese University of Hong Kong School of Data Science Shenzhen Research Institute of Big Data Shenzhen518172 China Institute of Systems Science Academy of Mathematics and System Science Chinese Academy of Sciences Key Laboratory of Systems and Control of Cas Beijing100190 China

ISBN: (纸本)9798350316339

The identification of continuous-time (CT) systems from discrete-time (DT) input and output signals, i.e., the sampled data, has received considerable attention for half a century. The state-of-the-art methods are parametric methods and thus subject to the typical issues of parametric methods. In the last decade, a major advance in system identification is the so-called kernel-based regularization method (KRM), which is free of the issues of parametric methods. It is interesting to test the potential of KRM on CT system identification. However, very few results have been reported, mainly because the estimators have no closed forms for general CT input signals, except for some very special cases. In this paper, we show for KRM that the estimators have closed forms when the DT input signal has the typical intersample behavior, i.e., zero-order hold or band-limited, and this paves the way for the application of KRM for CT system identification. Numerical Monte Carlo simulations show that the proposed method is more robust than the state-of-the-art methods and more accurate when the sample size is small. © 2024 IEEE.

关键词： Continuous time systems

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Multisymplectic Unscented Kalman Filter for Geometrically Exact Beams 1

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The 6th International Conference on Geometric science of Information, GSI 2023

作者： Li, Tianzhi Wang, Jinzhi Department of Mechanics and Engineering Science State Key Laboratory for Turbulence and Complex Systems Peking University Beijing100871 China

ISBN: (数字)9783031382994

ISBN: (纸本)9783031382987

This paper introduces an unscented Kalman filter for the dynamics of geometrically exact beams based on multisymplectic geometry and Hamel’s formalism for classical field theories. The presented approach is a field-theoretic analogue of the well-known unscented Kalman filter. The discrete-time propagation equations are derived from the discrete variational principle, rather than a direct discretization of the continuous-time differential equations. As such, the proposed estimation scheme respects intrinsic physical structures of mechanical systems and exhibits excellent numerical properties such as energy conservation and structure preservation. The approach is also applicable to a wide range of models characterized by one-dimensional field theory. Properties of the proposed estimation algorithm are illustrated with numerical simulations. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Differential equations

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