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A data representation method using distance correlation

Frontiers of Computer science 2025年第1期19卷 1-14页

作者： Xinyan LIANG Yuhua QIAN Qian GUO keyin ZHENG Institute of Big Data Science and Industry Shanxi UniversityTaiyuan 030006China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi UniversityTaiyuan 030006China School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and TechnologyTaiyuan 030024China

Association in-between features has been demonstrated to improve the representation ability of data. However, the original association data reconstruction method may face two issues: the dimension of reconstructed data is undoubtedly higher than that of original data, and adopted association measure method does not well balance effectiveness and efficiency. To address above two issues, this paper proposes a novel association-based representation improvement method, named as AssoRep. AssoRep first obtains the association between features via distance correlation method that has some advantages than Pearson’s correlation coefficient. Then an improved matrix is formed via stacking the association value of any two features. Next, an improved feature representation is obtained by aggregating the original feature with the enhancement matrix. Finally, the improved feature representation is mapped to a low-dimensional space via principal component analysis. The effectiveness of AssoRep is validated on 120 datasets and the fruits further prefect our previous work on the association data reconstruction.

关键词： association representation distance correlation classification

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data-driven Wasserstein distributionally robust chance-constrained optimization for crude oil scheduling under uncertainty

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Chinese Journal of Chemical Engineering 2024年第5期69卷 152-166页

作者： Xin Dai Liang Zhao Renchu He Wenli Du Weimin Zhong Zhi Li Feng Qian Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of EducationEast China University of Science and TechnologyShanghai 200237China Engineering Research Center of Process System Engineering Ministry of EducationEast China University of Science and TechnologyShanghai 200237China

Crude oil scheduling optimization is an effective method to enhance the economic benefits of oil *** uncertainties,including uncertain demands of crude distillation units(CDUs),might make the production plans made by the traditional deterministic optimization models infeasible.A data-driven Wasserstein distributionally robust chance-constrained(WDRCC)optimization approach is proposed in this paper to deal with demand uncertainty in crude oil ***,a new deterministic crude oil scheduling optimization model is developed as the basis of this *** Wasserstein distance is then used to build ambiguity sets from historical data to describe the possible realizations of probability distributions of uncertain demands.A cross-validation method is advanced to choose suitable radii for these ambiguity *** deterministic model is reformulated as a WDRCC optimization model for crude oil scheduling to guarantee the demand constraints hold with a desired high probability even in the worst situation in ambiguity *** proposed WDRCC model is transferred into an equivalent conditional value-at-risk representation and further derived as a mixed-integer nonlinear programming *** case studies from a real-world refinery are conducted to show the effectiveness of the proposed ***-of-sample tests demonstrate that the solution of the WDRCC model is more robust than those of the deterministic model and the chance-constrained model.

关键词： Distributions Model Optimization Crude oil scheduling Wasserstein distance Distributionally robust chance constraints

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Hybrid model of multimodal based on data enhancement and lumped reaction kinetics: Applying to industrial ebullated-bed residue hydrogenation unit

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Chinese Journal of Chemical Engineering 2025年第2期78卷 284-302页

作者： Jian Long Mengru Zhang Anlan Li Cheng Huang Dong Xue Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of EducationEast China University of Science and TechnologyShanghai 200237China Sinopec Engineering Incorporation Beijing 100101China

Industrial ebullated-bed is an important device for promoting the cleaning and upgrading of oil products. The lumped kinetic model is a powerful tool for predicting the product yield of the ebullated-bed residue hydrogenation (EBRH) unit, However, during the long-term operation of the device, there are phenomena such as low frequency of material property analysis leading to limited operating data and diverse operating modes at the same time scale, which poses a huge challenge to building an accurate product yield prediction model. To address these challenges, a data augmentation-based eleven lumped reaction kinetics mechanism model was constructed. This model combines generative adversarial networks, outlier elimination, and L2 norm data filtering to expand the dataset and utilizes kernel principal component analysis-fuzzy C-means for operating condition partitioning. Based on the hydrogenation reaction mechanism, a single and sub operating condition eleven lumped reaction kinetics model of an ebullated-bed residue hydrogenation unit, comprising 55 reaction paths and 110 parameters, was constructed before and after data augmentation. Compared to the single model before data enhancement, the average absolute error of the sub-models under data enhancement division was reduced by 23%. Thus, these findings can help guide the operation and optimization of the production process.

关键词： Mixed modeling Generative adversarial network Lumped kinetic model Multi-modal learning Ebullated-bed residue hydrogenation

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Formation control and path planning of multi-robot systems via large language models

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science China(Information sciences) 2025年第5期68卷 72-88页

作者： Dong XUE Xuanjie ZHOU Ming WANG Fangzhou LIU Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of EducationEast China University of Science and Technology Research Institute of Intelligent Control and Systems School of Astronautics Harbin Institute of Technology

Existing path planning and coordination control methods for multi-robot systems(MRS) typically rely on predefined rules and rudimentary algorithms. However, these methods often struggle to adapt flexibly to complex environments and to adjust motion targets appropriately. To address this challenge, this study presents a large language model(LLM)-assisted framework. By integrating textual descriptions of complex motion constraints, robot information, and local environmental data as inputs, LLMs generate motion objectives and translate them into executable control commands for the robots, thereby achieving coordinated control and path planning. This framework facilitates the generation, maintenance, and reshaping of formations in MRSs during path planning, applicable to both obstacle-free and obstacle-avoidance environments. Simulation results demonstrate that LLM-based control strategies enhance the autonomy, adaptability, flexibility, and robustness of MRS by processing complex information, making intelligent decisions, adapting to environmental changes, and handling disturbances and uncertainties.

关键词： multi-robot systems formation control path planning obstacle avoidance large language models

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Synergistic interaction of three-dimensional ZnCdS@MoS2 heterostructures for high-efficiency visible-light photocatalytic degradation of dyes

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Progress in Natural science:Materials International 2023年第4期33卷 442-449页

作者： Yingjiao Zhai Haowen He Mingze Xu Hongbin Zhao Jinhua Li Ministry of Education Key Laboratory for Cross-Scale Micro and Nano Manufacturing Nanophotonics and Biophotonics Key Laboratory of Jilin Province State Key Laboratory of High Power Semiconductor Lasers Changchun University of Science and Technology State Key Laboratory of Advanced Materials for Smart Sensing General Research Institute for Nonferrous Metals

High-performance photocatalysts should urgently be developed to efficiently solve serious environmental problems. Herein, we successfully synthesized a novel, visible-light activated, three-dimensional ZnCdS@MoS2photocatalyst using a simple, two-step hydrothermal process. Based on reaction time-dependence, the growth mechanism of “oriented attachment” of the ZnCdS@MoS2heterostructure is described in detail. The photodegradation of methylene blue（MB） indicated that the prepared sample has an outstanding photocatalytic activity owing to the accelerated photogenerated electron production rate, inhibited electron–hole pair recombination,improved visible light–utilization rate, and increased specific surface area. According to our experimental results,the degradation rate of MB using the ZnCdS@MoS2nanocomposite was as high as 99.3% after 100 min of visible-light irradiation. The photocatalyst remained stable without any obvious decrease in activity even after three cycles of use. This study provides a strong support for the design of high-performance photocatalysts and their application in optoelectronic devices.

关键词： ZnCdS@MoS2 Heterostructure Photocatalytic Visible-light Dye

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Two efficient beamforming methods for hybrid IRS-aided AF relay wireless networks

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science China(Information sciences) 2025年第4期68卷 363-379页

作者： Xuehui WANG Qingbo LI Wen ZHU Feng SHU Mengxing HUANG Fuhui ZHOU Riqing CHEN Cunhua PAN Yongpeng WU Jiangzhou WANG School of Mathematics and Statistics Hainan Normal University Key Laboratory of Data Science and Smart Education Ministry of Education Hainan Normal University School of Information and Communication Engineering Hainan University Collaborative Innovation Center of Information Technology Hainan University School of Electronic and Optical Engineering Nanjing University of Science and Technology College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Digital Fujian Institute of Big Data for Agriculture Fujian Agriculture and Forestry University National Mobile Communications Research Laboratory Southeast University Shanghai Key Laboratory of Navigation and Location Based Services Shanghai Jiao Tong University School of Engineering University of Kent

Owing to its ability to mitigate the double-fading effect by amplifying the reflected signal, the active intelligent reflecting surface(IRS) has garnered significant attention. In this paper, an amplify-and-forward(AF) relay network assisted by a hybrid IRS consisting of both passive and active units is developed. A signal-to-noise ratio(SNR) maximization problem is formulated, where the AF relay beamforming matrix and the hybrid IRS reflecting coefficient matrices for two-time slots need to be optimized. To address the SNR maximization problem, this paper proposes both a high-performance(HP) method and a low-complexity(LC) method. The HP method is based on the semidefinite relaxation and fractional programming(SDR-FP)algorithm, with rank-1 solutions obtained through Gaussian randomization. For the LC method, the amplification coefficient of each active IRS element is assumed to be equal. The SNR maximization problem is then addressed using the whitening filter,generalized power iteration, and generalized Rayleigh-Ritz(WF-GPI-GRR) approach. Simulation results show that compared with the benchmarks, such as the passive IRS-aided AF relay network, the proposed HP-SDR-FP and WF-GPI-GRR methods achieve significant rate improvements. In particular, the HP-SDR-FP and WF-GPI-GRR methods yield more than a 135.0%rate gain when the transmit power Ps of the source is 10 dBm. Furthermore, the proposed HP-SDR-FP method outperforms the WF-GPI-GRR method in terms of rate performance.

关键词： double-fading intelligent reflecting surface active elements passive elements hybrid IRS AF relay

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Research on Bitcoin Address Classification Based on Transaction History Features 3

Research on Bitcoin Address Classification Based on Transact...

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3rd International Seminar on Artificial Intelligence, Networking, and Information Technology, AINIT 2022

作者： Qin, Lu Yi, Li Lin, Xiancheng Luo, Ziqiang School of Information Science and Technology Hainan Normal University Haikou571158 China Key Laboratory of Data Science Smart Education of Ministry of Education Haikou571158 China

ISBN: (数字)9781510662971

ISBN: (纸本)9781510662964

As the most popular cryptocurrency now, Bitcoin's transaction data is easy to obtain, so de-anonymizing Bitcoin becomes possible. This paper constructs a data set of Bitcoin addresses including 5 categories, analyzes and extracts the transaction features of Bitcoin addresses in more detail based on related work, and proposes two new features of fourth-order transaction moments and sample distribution. New features improve the performance of Bitcoin address classification. The accuracy of the LightGBM model was 0.94 and the F1 score was 0.91. This method can identify unknown types of Bitcoin addresses, which improves the ability of relevant agencies to investigate Bitcoin illegal activities. © 2023 SPIE.

关键词： Machine learning

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Application of XGBoost and kernel principal component analysis to forecast oxygen content in ESR

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Journal of Iron and Steel Research International 2024年第12期31卷 2940-2952页

作者： Yu-xiao Liu Yan-wu Dong Zhou-hua Jiang Qi Wang Yu-shuo Li School of Metallurgy Northeastern UniversityShenyang110819LiaoningChina National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern UniversityShenyang110819LiaoningChina Key Laboratory of Data Analytics and Optimization for Smart Industry(Northeastern University) Ministry of EducationShenyang110000LiaoningChina Key Laboratory of Ecological Metallurgy of Multimetallic Mineral Northeastern UniversityShenyang110819LiaoningChina

A model combining kernel principal component analysis(KPCA)and Xtreme Gradient Boosting(XGBoost)was introduced for forecasting the final oxygen content of electroslag *** was employed to reduce the dimensionality of the factors influencing the endpoint oxygen content and to eliminate any existing correlations among these *** resulting principal components were then utilized as input variables for the XGBoost prediction *** KPCA-XGBoost model was trained and proven using data obtained from *** model structure was adapted,and hyperparameters were optimized using grid search *** model performance of the KPCA-XGBoost model is compared with five machine learning models,including the support vector regression *** findings demonstrated that the KPCA-XGBoost model exhibited the highest level of prediction accuracy,indicating that the incorporation of KPCA significantly enhanced the regression prediction performance of the *** accuracy of the KPCA-XGBoost model was 82.4%,97.1%,and 100%at errors of±1.5×10^(-6),±2.0×10^(-6),and±3×10^(-6)for oxygen content,respectively.

关键词： Electroslag remelting Oxygen content Machine learning Kernel principal component analysis XGBoost

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Optimization of LiB electrode with bi-diameter active particles using a microstructure-resolved model

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Particuology 2024年第12期95卷 49-61页

作者： Peng Zhang Junqi Weng Ping Li Guanghua Ye Wenli Du Xinggui Zhou Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of EducationEast China University of Science and TechnologyShanghai200237China BASF(China)Company Ltd. Shanghai200137China State Key Laboratory of Chemical Engineering School of Chemical EngineeringEast China University of Science and TechnologyShanghai200237China

The microstructure of electrodes significantly affects the performance of lithium-ion batteries(LiBs),and using bi-diameter active particles is a simple but effective way to regulate the microstructure of commercial LiB ***,to optimize the LiB cathode of bi-diameter active particles,a microstructure-resolved model is developed and *** results indicate that randomly packing of bi-diameter active particles is optimal when the electrolyte diffusion limitation is mild,as it provides the highest volume fraction of active *** strong electrolyte diffusion limitations,layered packing with small particles near the separator is *** is because particles near the current collector have a low lithiation ***,optimizing the random packing can further improve the energy *** energy-oriented LiBs,a low volume fraction of small particles(0.2)is preferred due to the higher volume fraction of active *** power-oriented LiBs,a high volume fraction of small particles(0.8)is better because it reduces diffusion *** work should serve to guide the optimal design of electrode microstructure for achieving high-performance LiBs.

关键词： Lithium-ion battery Electrode microstructure Bi-diameter particles Diffusion limitation Microstructure-resolved model

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A Product Selection Calculation Method Based on the Similarity of Psychological Expectation Cloud Models 24

A Product Selection Calculation Method Based on the Similari...

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2024 International Conference on Cloud Computing and Big data, ICCBD 2024

作者： Guo, Xuepin Cui, Xinyu Lu, Zhaohui Luo, Ziqiang School of Information Science and Technology Hainan Normal University China Key Laboratory of Data Science and Education of Ministry of Education China

ISBN: (纸本)9798400710223

Faced with a wide variety of products offered by merchants, consumers often find it challenging to make scientifically informed purchasing decisions. Consumers tend to focus only on one or a few indicators while neglecting a comprehensive evaluation of the various attributes of the products, leading to biased and less rational purchasing behaviors. Considering the uncertainties in product selection, this paper adopts the cloud model theory and proposes a product selection calculation method based on the similarity of psychological expectation cloud models. Through examples, we derive the corresponding quantitative values and perform cloud map simulation analysis, demonstrating the feasibility and rationality of this method. This provides consumers with a scientific approach to product selection. © 2024 ACM.

关键词： Calculation Cloud model Product selection Psychological expectation value Similarity

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