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第43届中国控制会议

作者： He Yueyang Liang Xiuxia Xia Manman Liang Tao School of Artificial Intelligence and Data Science Hebei University of Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Batch process is an indispensable part of modern industrial production. Because of its flexibility and high efficiency,it is widely used in biopharmaceutical,wastewater treatment,fine chemical and other fields. Due to the improvement of modern equipment and the expansion of the scale of the factory,the chance of accidents and failures also showed an exponential increase trend. Therefore,fault diagnosis is very important to ensure the stability and safety of chemical processes. In order to improve the safety and reliability of batch process,an improved Genetic Algorithm is proposed to optimize the parameters of Separable Convolution Network and Temporal Convolutional Network(SeparableConv1D-TCN) for fault diagnosis of batch process. Firstly,the SeparableConv1D-TCN network is constructed to extract features from the original intermittent data and perform fault diagnosis by standardizing the intermittent data. In order to find the network parameters with high diagnostic accuracy,the genetic algorithm with good point set method and optimal domain search is used to optimize. The effectiveness of the method is verified by simulation experiments and comparative experiments with penicillin experimental data.

关键词： Batch Process Fault Diagnosis SeparableConv1D-TCN Genetic Algorithm

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Benefit of aerosol reduction to winter wheat during China's clean air action:A case study of Henan Province

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Journal of Environmental sciences 2024年第7期141卷 90-101页

作者： Yang Bai Pengfei Zhao Xueyang Chen Lijun Wang Wenjuan Chang Jianzhong Guo Jiayao Wang College of Geography and Environmental Science Henan UniversityKaifeng 475004China Henan Industrial Technology Academy of Spatio-Temporal Big Data Henan UniversityZhengzhou 475000China Henan Technology Innovation Center of Spatio-Temporal Big Data Henan UniversityZhengzhou 450046China Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions(Henan University) Ministry of EducationKaifeng 475000China

A strongly declining aerosol radiative effect has been observed in China since 2013 after implementing the clean air action,yet its impact on wheat(Triticum aestivum L.)production remains *** use satellite measures and a biophysical crop model to assess the impact of aerosol-induced radiative perturbations on winter wheat production in the agricultural belt of Henan province from 2013 to *** calibrating parameters with the extended Fourier Amplitude Sensitivity Test(EFAST)and the generalized likelihood uncertainty estimation(GLUE)method,the DSSAT CERES-Wheat model was able to simulate crop biomass and yield more *** found that the aerosol negatively impacted wheat biomass by 21.87%and yield by 22.48%from 2006 to 2018,and the biomass effects from planting to anthesis were more significant compared to anthesis to *** to the strict clean air action,under all-sky conditions,the surface solar shortwave radiation(SSR)in 2018 increased by about 7.08%over 2006-2013 during the wheat growing *** a result of the improvement of crop photosynthesis,winter wheat biomass and yield increased by an average of 5.46%and 2.9%,*** findings show that crop carbon uptake and yield will benefit from the clean air action in China,helping to ensure national food and health security.

关键词： Haze pollution Clean air action Aerosol-induced radiative perturbation Wheat production DSSAT model

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Proximal Policy Optimization-Based Intrusion Detection Model for Satellite Networks 5

Proximal Policy Optimization-Based Intrusion Detection Model...

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5th International Symposium on Computer Engineering and Intelligent Communications, ISCEIC 2024

作者： Liang, Shuaishuai Feng, Huibin Wu, Jiawei He, Canfei College of Computer and Data Science Fuzhou University Fuzhou China School of Computer and Big Data Minjiang University Fuzhou China

ISBN: (纸本)9798331518677

As satellite network communication systems become an increasingly pivotal role in modern life, The routine maintenance of satellite networks is challenging due to limited resources and their susceptibility to interference. Satellite networks are more reliant on interruption detection systems than terrestrial networks. The importance of interruption detection systems in satellite networks is becoming increasingly evident. In this paper, we propose a novel intrusion detection model based on Proximal Policy Optimization (PPO). This method interacts with the environment to learn and optimize detection strategies, allowing for dynamic adaptation to environmental changes and attack patterns. We evaluated our model on benchmark datasets and compared its performance metrics against existing models. The results demonstrate that our proposed model significantly enhances overall accuracy and precision, and outperforms baseline models in terms of training convergence speed. © 2024 IEEE.

关键词： Network intrusion

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Robust Federated Learning for Privacy Preservation and Efficiency in Edge Computing

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IEEE Transactions on Services Computing 2025年第3期18卷 1739-1752页

作者： Zhou, Hao Dai, Hua Yang, Geng Xiang, Yang Nanjing University of Posts and Telecommunications School of Computer Science Jiangsu Security and Intelligent Processing Lab of Big Data Nanjing210023 China Swinburne University of Technology Department of Computer Science HawthornVIC3122 Australia

Federated Learning (FL) has emerged as a key enabler of privacy-preserving distributed model training in edge computing environments, crucial for service-oriented applications such as personalized healthcare, smart cities, and intelligent assistants. However, existing privacy-preserving FL methods are susceptible to multiple privacy leakage attacks (MPLA), where adversaries infer sensitive information through repeated gradient updates. This paper proposes a Robust and Communication-Efficient Federated Learning (RCFL) framework designed to enhance privacy protection and communication efficiency in edge-based service environments. RCFL integrates a global privacy-preserving mechanism with an innovative privacy encoding strategy that minimizes privacy risks over multiple data releases while significantly reducing communication overhead. The proposed framework's theoretical analysis demonstrates its ability to maintain differential privacy across numerous interactions, ensuring robust model convergence and efficiency. Experimental results using MNIST and CIFAR-10 datasets reveal that RCFL can lower the MPLA success rate from 88.56% to 42.57% compared to state-of-the-art methods, while reducing communication costs by over 90%. These findings underscore RCFL's potential to enhance security, efficiency, and scalability in service-oriented edge computing applications. © 2008-2012 IEEE.

关键词： Federated learning

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High Performance Electrically Small Huygens Rectennas Enable Wirelessly Powered Internet of Things Sensing Applications:A Review

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Engineering 2022年第4期8卷 42-59页

作者： Wei Lin Richard W.Ziolkowski Global Big Data Technologies Centre School of Electrical and Data EngineeringUniversity of Technology SydneyUltimoNSW 2007Australia

Far-field wireless power transfer(WPT)is a major breakthrough technology that will enable the many anticipated ubiquitous Internet of Things(IoT)applications associated with fifth generation(5G),sixth generation(6G),and beyond wireless ***,which are the combination of rectifying circuits and antennas,are the most critical components in far-field WPT ***,compact application devices require even smaller integrated rectennas that simultaneously have large electromagnetic wave capture capabilities,high alternating current(AC)-to-direct current(DC)(AC-to-DC)conversion efficiencies,and facilitate a multifunctional wireless *** paper reviews various rectenna miniaturization techniques such as meandered planar inverted-F antenna(PIFA)rectennas;miniaturized monopole-and dipole-based rectennas;fractal loop and patch rectennas;dielectric-loaded rectennas;and electrically small near-field resonant parasitic *** performance characteristics are summarized and then compared with our previously developed electrically small Huygens rectennas that are proven to be more suitable for IoT *** have been tailored,for example,to achieve batteryfree IoT sensors as is demonstrated in this ***-free,wirelessly powered devices are smaller and lighter in weight in comparison to battery-powered ***,they are environmentally friendly and,hence,have a significant societal benefit.A series of high-performance electrically small Huygens rectennas are presented including Huygens linearly-polarized(HLP)and circularly-polarized(HCP)rectennas;wirelessly powered IoT sensors based on these designs;and a dual-functional HLP rectenna and antenna ***,two linear uniform HLP rectenna array systems are considered for significantly larger wireless power *** arrays illustrate how they can be integrated advantageously with DC or radio frequency(RF)power-combining schemes for practical IoT applications.

关键词： Antenna Array Cardioid pattern Electrically small antenna Huygens dipole antenna Internet of Things(IoT) Rectenna Rectifier circuit Wireless power transfer

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NEURON-ENHANCED AUTOENCODER MATRIX COMPLETION AND COLLABORATIVE FILTERING: THEORY AND PRACTICE 12

NEURON-ENHANCED AUTOENCODER MATRIX COMPLETION AND COLLABORAT...

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12th International Conference on Learning Representations, ICLR 2024

作者： Fan, Jicong Chen, Rui Zhang, Zhao Ding, Chris H.Q. School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China School of Computer Science & Information Engineering Hefei University of Technology China

Neural networks have shown promising performance in collaborative filtering and matrix completion but the theoretical analysis is limited and there is still room for improvement in terms of the accuracy of recovering missing values. This paper presents a neuron-enhanced autoencoder matrix completion (AEMC-NE) method and applies it to collaborative filtering. Our AEMC-NE adds an element-wise autoencoder to each output of the main autoencoder to enhance the reconstruction capability. Thus it can adaptively learn an activation function for the output layer to approximate possibly complicated response functions in real data. We provide theoretical analysis for AEMC-NE as well as AEMC to investigate the generalization ability of autoencoder and deep learning in matrix completion, considering both missing completely at random and missing not at random. We show that the element-wise neural network has the potential to reduce the generalization error bound, the data sparsity can be useful, and the prediction performance is closely related to the difference between the numbers of variables and samples. The numerical results on synthetic data and five benchmark datasets demonstrated the effectiveness of AEMC-NE in comparison to many baselines. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Collaborative filtering

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Meshfree-based physics-informed neural networks for the unsteady Oseen equations

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Chinese Physics B 2023年第4期32卷 151-159页

作者：彭珂依岳靖张文李剑 School of Mathematics and Data Science Shaanxi University of Science and TechnologyXi’an 710021China School of Electrical and Control Engineering Shaanxi University of Science and TechnologyXi’an 710021China

We propose the meshfree-based physics-informed neural networks for solving the unsteady Oseen ***,based on the ideas of meshfree and small sample learning,we only randomly select a small number of spatiotemporal points to train the neural network instead of forming a ***,we optimize the neural network by minimizing the loss function to satisfy the differential operators,initial condition and boundary ***,we prove the convergence of the loss function and the convergence of the neural *** addition,the feasibility and effectiveness of the method are verified by the results of numerical experiments,and the theoretical derivation is verified by the relative error between the neural network solution and the analytical solution.

关键词： physics-informed neural networks the unsteady Oseen equation convergence small sample learning

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Prediction model for mortality rate in sepsis patients based on the MIMIC IV database 23

Prediction model for mortality rate in sepsis patients based...

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2023 International Conference on Intelligent Computing and Its Emerging Applications, ICEA 2023

作者： Fu, Teng Zhang, Xuan Tseng, Shih-Pang Huang, Lixin School of Information Science and Technology Sanda University Shanghai China School of Software and Big Data Changzhou College of Information Technology Changzhou China

ISBN: (纸本)9798400709050

Sepsis is a serious infectious disease. Toxins released by bacteria enter the blood circulation and cause systemic inflammatory response. Therefore, the timely diagnosis and treatment of sepsis is very important. In this study, a sepsis death prediction model was designed based on the clinical data of patients with sepsis in the mimic IV database. Firstly, the extracted data are preprocessed by missing value processing, data normalization and other work, and the key features are extracted. Then, an integrated algorithm model based on xgboost, random forest and gbdt is designed and compared with a single model to verify the effectiveness of the model. The experimental results show that the accuracy of the integrated model is 93.017%, which is about 2.47%, 0.31%, 0.94% and 3.78% higher than the single prediction model xgboost, random forest, gbdt and SVM, respectively. It has practical significance for the clinical management and personalized treatment strategy of patients with sepsis, and also provides ideas for the development of clinical decision support system. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Personalized medicine

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On PM t chart for monitoring changes in location parameter 3

On PM t chart for monitoring changes in location parameter

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3rd International Conference on Computer Vision and data Mining, ICCVDM 2022

作者： Sajjad, Muhammad Kamran Li, Dequan Amir, M. Wasim Selhaba, Ayesha School of Mathematics and Big Data Anhui University of Science and Technology Huainan232001 China

ISBN: (纸本)9781510661363

The Progressive Mean (PM) control chart is a widely recognized tool to notice the insignificant and standard variations in the process location parameter. There is one deficiency in PM chart, it generates signals which are out of control, and when the standard deviation is processed this deficiency changes the results. To overcome this problem, we proposed a method in case of not stable process stand deviation chart is used, which enables monitoring of process which is more robust in this case. The suggested chart is a participant for and charts. The numerical results concluded that the performance of the proposed chart is superior to detect small-scale and standard changes in the process parameter. To support the study an expressive application is also provided. © COPYRIGHT SPIE.

关键词： Stability criteria

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Graph dynamical networks for forecasting collective behavior of active matter

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Chinese Physics B 2022年第11期31卷 438-444页

作者： Yanjun Liu Rui Wang Cai Zhao Wen Zheng Institute of Public-Safety and Big Data College of Data ScienceTaiyuan University of TechnologyTaiyuan 030024China Center of Information Management and Development Taiyuan University of TechnologyTaiyuan 030024China Center for Healthy Big Data Changzhi Medical CollegeChangzhi 046000China

After decades of theoretical studies,the rich phase states of active matter and cluster kinetic processes are still of research *** to efficiently calculate the dynamical processes under their complex conditions becomes an open ***,machine learning methods have been proposed to predict the degree of coherence of active matter *** this way,the phase transition process of the system is quantified and *** this paper,we use graph network as a powerful model to determine the evolution of active matter with variable individual velocities solely based on the initial position and state of the *** graph network accurately predicts the order parameters of the system in different scale models with different individual velocities,noise and density to effectively evaluate the effect of diverse *** with the classical physical deduction method,we demonstrate that graph network prediction is excellent,which could save significantly computing resources and *** addition to active matter,our method can be applied widely to other large-scale physical systems.

关键词： active matter graph network improvement of Vicsek collective motion

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