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13th IEEE data Driven Control and Learning Systems Conference, DDCLS 2024

作者： Liu, Yundan Zhang, Kai Zhang, Xiaowen University of Science and Technology Beijing School of Automation and Electrical Engineering Beijing100083 China Ministry of Education Key Laboratory of Knowledge Automation for Industrial Processes Beijing100083 China

ISBN: (纸本)9798350361674

In a hot strip rolling mill (HSRM) process, the control of the crown quality of a strip steel is of paramount importance. In this paper, a distributed crown prediction model based on an improved Variational Autoencoder (VAE) framework is proposed. The model integrates a multi -layer VAE architecture to better accommodate the complex distribution of actual production data. Utilizing real production data from Ansteel's HSRM process, the model was subjected to extensive training and validation to optimize its predictive accuracy. Then in comparison with centralized VAE and VAE models with specialized prediction layers, the distributed model demonstrated exceptional predictive performance, with evaluation metrics including mean absolute error, root mean squared error and mean absolute percentage error. It can be seen from the application result that: the proposed method shows a better prediction result compared with the traditional methods. © 2024 IEEE.

关键词： Prediction models

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Propolis inspired sunscreens for efficient UV-protection and skin barrier maintenance

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Nano Research 2022年第9期15卷 8237-8246页

作者： Yu Fu Ruoyu Wan Lei Yang Lidan Xiong Junfei Hu Jie Tang Hailun He Zhipeng Gu Li Li Yiwen Li College of Polymer Science and Engineering State Key Laboratory of Polymer Materials EngineeringSichuan UniversityChengdu 610065China Cosmetics Safety and Efficacy Evaluation Center Department of DermatologyWest China HospitalSichuan UniversityChengdu 610041China NMPA Key Laboratory for Human Evaluation and Big Data of Cosmetics Chengdu 610041China

The direct use of naturally occurring,small molecular ingredients in bioinspired sunscreens has raised several concerns due to the instability,photocytotoxicity,and potential blood toxicity of those *** this work,we have employed natural ultraviolet(UV)-blocking molecule caffeic acid phenethyl ester(CAPE)from propolis to prepare poly(CAPE)nanoparticles(NPs)as the main bioactive ingredient to fabricate propolis-inspired hydrogel *** with small molecular CAPE,poly(CAPE)NPs exhibited better dispersion and stability in water,as well as lower physiological toxicity and skin *** the resulting composite hydrogels demonstrated promising properties including water-resistant whereas can be easily erased by warm water as well as safety when interacting with *** importantly,the hydrogel sunscreens showed excellent UV protection properties both in vitro and in vivo,and the positive effects in maintaining skin barrier *** work provides new strategies towards the facile construction of nature-inspired robust sunscreens in the future.

关键词： bioinspired sunscreen composite hydrogel ultraviolet protection

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Fully-Decoupled and Second-Order Time-Accurate Scheme for the Cahn–Hilliard Ohta–Kawaski Phase-Field Model of Diblock Copolymer Melt Confined in Hele–Shaw Cell

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Communications in Mathematics and Statistics 2024年第3期12卷 479-504页

作者： Junying Cao Jun Zhang Xiaofeng Yang School of Data Science and Information Engineering Guizhou Minzu UniversityGuiyang 550025People’s Republic of China Guizhou Key Laboratory of Big Data Statistics Analysis Computational Mathematics Research CenterGuizhou University of Finance and EconomicsGuiyang 550025People’s Republic of China Department of Mathematics University of South CarolinaColumbiaSCUSA

In this work,we consider numerical approximations of the phase-field model of diblock copolymer melt confined in Hele–Shaw cell,which is a very complicated coupled nonlinear system consisting of the Darcy equations and the Cahn–Hilliard type equations with the Ohta–Kawaski *** the combination of a novel explicit-Invariant Energy Quadratization approach and the projection method,we develop the first full decoupling,energy stable,and second-order time-accurate numerical *** introduction of two auxiliary variables and the design of two auxiliary ODEs play a vital role in obtaining the full decoupling structure while maintaining energy *** scheme is also linear and unconditional energy stable,and the practical implementation efficiency is also very high because it only needs to solve a few elliptic equations with constant coefficients at each time *** strictly prove that the scheme satisfies the unconditional energy stability and give a detailed implementation *** experiments further verify the convergence rate,energy stability,and effectiveness of the developed algorithm.

关键词： Phase-field Explicit-IEQ Darcy Decoupled Energy Stability Diblock copolymer melt

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Rethinking-based Code Summarization with Chain of Comments 31

Rethinking-based Code Summarization with Chain of Comments

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31st International Conference on Computational Linguistics, COLING 2025

作者： Cao, Liuwen He, Hongkui Huang, Hailin Wang, Jiexin Cai, Yi School of Software Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China

ISBN: (纸本)9798891761964

Automatic code summarization aims to generate concise natural language descriptions (summary) for source code, which can free software developers from the heavy burden of manual commenting and software maintenance. Existing methods focus on learning a direct mapping from pure code to summaries, overlooking the significant heterogeneity gap between code and summary. Moreover, existing methods lack a human-like re-check process to evaluate whether the generated summaries match well with the code. To address these two limitations, we introduce RBCoSum, a novel framework that incorporates the generated Chain Of Comments (COC) as auxiliary intermediate information for the model to bridge the gap between code and summaries. Also, we propose a rethinking process where a learned ranker trained on our constructed ranking dataset scores the extent of matching between the generated summary and the code, selecting the highest-scoring summary to achieve a re-check process. We conduct extensive experiments to evaluate our approach and compare it with other automatic code summarization models as well as multiple code Large Language Models (LLMs). The experimental results show that RBCoSum is effective and outperforms baselines by a large margin. The human evaluation also proves the summaries generated with RBCoSum are more natural, informative, useful, and truthful. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Privacy-Preserving Average Consensus for Directed Graphs via State Decomposition and External Input 43

Privacy-Preserving Average Consensus for Directed Graphs via...

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43rd Chinese Control Conference, CCC 2024

作者： Wang, Jimin Xu, Mengjie University of Science and Technology Beijing School of Automation and Electrical Engineering Beijing100083 China Ministry of Education Key Laboratory of Knowledge Automation for Industrial Processes Beijing100083 China

ISBN: (纸本)9789887581581

This paper investigates the privacy-preserving average consensus problem for directed graphs. A privacy-preserving method is designed based on the state decomposition and external input. Each node arbitrarily decomposes its initial state into two substates, one of the substates is exchanged with neighbors and the other substate is retained to prevent leakage of initial state information. To reduce the communication burdens, a new method instead of the push-sum algorithm is provided by introducing the external input. Based on this, the average consensus is achieved for the strongly connected graph. A privacy parameter during the decomposition process is designed to meet the different privacy needs of different nodes. Then, we show that this algorithm can effectively protect the privacy of the node's initial state, and meet the different privacy needs of different nodes. Finally, the validity of the results is verified through a numerical example. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Differential privacy

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Fine-grained Personalized Hierarchical Federated Learning towards Heterogeneous Internet of Vehicles

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IEEE Internet of Things Journal 2025年第11期12卷 16349-16362页

作者： Zhang, Puning Shao, Shuman Wu, Chao Yang, Zhigang Zhang, Ruoying Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China Chongqing Key Laboratory of Ubiquitous Sensing and Networking Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China Chongqing400065 China Chongqing Innovation Center of Industrial Big-Data Co. Ltd National Engineering Laboratory for Industrial Big-data Application Technology Chongqing400707 China China Merchants Testing Vehicle Technology Research Institute Co. Ltd Chongqing404100 China Chongqing University of Posts and Telecommunications School of Cyber Security and Information Law Chongqing400065 China Chongqing Key Laboratory of Ubiquitous Sensing and Networking Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China China China Academy of Information and Communications Beijing100083 China

High mobility and non-independent and identically distributed (non-iid) data in the Internet of Vehicles (IoV) pose challenges for the convergence speed and accuracy of federated learning model training. Traditional federated learning approaches fail to account for the unique characteristics of local vehicle data, limiting the model performances. To address this, we propose a personalized hierarchical federated learning method tailored for heterogeneous IoV. Our approach customizes models for each vehicle, enhancing their performance on local data. We introduce a network-aware client selection method that dynamically chooses vehicle clients based on their spatiotemporal distribution and changing channel conditions. Additionally, our fine-grained personalized learning method leverages frequency domain analysis to extract and retain both fundamental and personalized knowledge from local model parameters. This allows us to build highly accurate personalized models suited to each vehicle's data characteristics. Experimental results show that our method improves model accuracy by up to 10.31% and reduces training time by up to 24.93% compared to the latest personalized federated learning algorithms. © 2025 IEEE.

关键词： Federated learning

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Byzantine-robust distributed support vector machine

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Science China Mathematics 2025年第3期68卷 707-728页

作者： Xiaozhou Wang Weidong Liu Xiaojun Mao School of Statistics East China Normal UniversityShanghai 200062China Key Laboratory of Advanced Theory and Application in Statistics and Data Science(Ministry of Education) East China Normal UniversityShanghai 200062China School of Mathematical Sciences Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of Artificial Intelligence(Ministry of Education) Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of Scientific and Engineering Computing(Ministry of Education) Shanghai Jiao Tong UniversityShanghai 200240China

The development of information technology brings diversification of data sources and large-scale data sets and calls for the exploration of distributed learning algorithms. In distributed systems, some local machines may behave abnormally and send arbitrary information to the central machine(known as Byzantine failures), which can invalidate the distributed algorithms based on the assumption of faultless systems. This paper studies Byzantine-robust distributed algorithms for support vector machines(SVMs) in the context of binary classification. Despite a vast literature on Byzantine problems, much less is known about the theoretical properties of Byzantine-robust SVMs due to their unique challenges. In this paper, we propose two distributed gradient descent algorithms for SVMs. The median and trimmed mean operations in aggregation can effectively defend against Byzantine failures. Theoretically, we show the convergence of the proposed estimators and provide the statistical error rates. After a certain number of iterations, our estimators achieve near-optimal rates. Simulation studies and real data analysis are conducted to demonstrate the performance of the proposed Byzantine-robust distributed algorithms.

关键词： Byzantine robustness convergence distributed learning support vector machine

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Stability of connected and automated vehicles platoon considering communications failures

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Chinese Physics B 2023年第7期32卷 598-609页

作者：刘润坤于海洋任毅龙崔志勇 School of Transportation Science and Engineering Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety ControlBeihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100191China

As a form of a future traffic system,a connected and automated vehicle(CAV)platoon is a typical nonlinear physical *** can communicate with each other and exchange ***,communication failures can change the platoon system *** characterize this change,a dynamic topology-based car-following model and its generalized form are proposed in this ***,a stability analysis method is ***,taking the dynamic cooperative intelligent driver model(DC-IDM)for example,a series of numerical simulations is conducted to analyze the platoon stability in different communication topology *** results show that the communication failures reduce the stability,but information from vehicles that are farther ahead and the use of a larger desired time headway can improve ***,the critical ratio of communication failures required to ensure stability for different driving parameters is studied in this work.

关键词： connected and automated vehicle car-following model communication failures string stability

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Two-Stage Degradation Modeling and Remaining Useful Life Prediction Method Based on Uncertain Random Process 14

Two-Stage Degradation Modeling and Remaining Useful Life Pre...

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14th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023

作者： Cao, Xuerui Peng, Kaixiang Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9798350301359

Remaining useful life (RUL) prediction is the foundation and core of prognosis and health management to improve the reliability of the device. For a category of devices with two-stage degradation characteristics, the aleatory and epistemic uncertainty caused by inherent fluctuations within the system and insufficient sample knowledge is ignored frequently. Therefore, this paper proposes a novel two-stage degradation model and RUL prediction method considering aleatory and epistemic uncertainty. Firstly, a two-stage uncertain random process is established to characterize the degradation trajectory of the device, and the analytical expression of the RUL is derived under the concept of the first hitting time. Afterwards, the modified information criterion is employed to detect the change-point. Meanwhile, the stochastic uncertainty maximum likelihood estimation algorithm and Bayesian update are used to achieve parameter identification. Finally, the practicability of the proposed method is verified by Monte Carlo simulation. © 2023 IEEE.

关键词： aleatory uncertainty epistemic uncertainty RUL prediction two-stage degradation model

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Rockburst prediction using artificial intelligence techniques:A review

Rock Mechanics Bulletin

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Rock Mechanics Bulletin 2024年第3期3卷 1-13页

作者： Yu Zhang Kongyi Fang Manchao He Dongqiao Liu Junchao Wang Zhengjia Guo School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and ArchitectureBeijing100044China State Key Laboratory for Geomechanics and Deep Underground Engineering China University of Mining and TechnologyBeijing100083China Thomas Lord Department of Computer Science University of Southern CaliforniaLos Angeles90007CAUSA

Rockburst is a phenomenon where sudden,catastrophic failure of the rock mass occurs in underground deep regions or areas with high tectonic stress during the excavation *** disasters endanger the safety of people's lives and property,national energy security,and social interests,so it is very important to accurately predict *** rockburst prediction has not been able to find an effective prediction method,and the study of the rockburst mechanism is facing a *** the development of artificial intelligence(AI)techniques in recent years,more and more experts and scholars have begun to introduce AI techniques into the study of the rockburst *** previous research,several scholars have attempted to summarize the application of AI techniques in rockburst ***,these studies either are not specifically focused on reviews of the application of AI techniques in rockburst prediction,or they do not provide a comprehensive *** on the advantages of extensive interdisciplinary research and a deep understanding of AI techniques,this paper conducts a comprehensive review of rockburst prediction methods leveraging AI ***,pertinent definitions of rockburst and its associated hazards are ***,the applications of both traditional prediction methods and those rooted in AI techniques for rockburst prediction are summarized,with emphasis placed on the respective advantages and disadvantages of each ***,the strengths and weaknesses of prediction methods leveraging AI are summarized,alongside forecasting future research trends to address existing challenges,while simultaneously proposing directions for improvement to advance the field and meet emerging demands effectively.

关键词： Rockburst Rockburst prediction Artificial intelligence techniques

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