检索结果-内蒙古大学图书馆

International Conference on Industrial Artificial Intelligence (IAI)

作者： Yating Fu Junhao Tian Hui Yang State Key Laboratory of Performance Monitoring and Protecting of Rail Transit Infrastructure East China Jiaotong University Nanchang China Key Laboratory of Advanced Control and Optimization of Jiangxi Province East China Jiaotong University Nanchang China School of Electrical and Automation Engineering East China Jiaotong University Nanchang China

In order to meet the operational requirements of the high-speed train in terms of safety, accuracy and smoothness, an adaptive backstepping sliding mode control approach based on fuzzy regulation is put forward in this paper. Firstly, to solve the problem of insufficient accuracy of the single point model, this paper establishes a mass point dynamics model of the high-speed train and sets a compensation term for the model considering the uncertainties in the operation process. Secondly, the adaptive law is designed to estimate the compensation term and the new backstepping sliding mode control strategy is developed. Finally, fuzzy rules are designed to adjust the switching gain of the sliding mode term to suppress jitter. The results of simulation and comparison experiments demonstrate the effectiveness and feasibility of the proposed modeling and control method.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Extraction of binary neutron star gravitational wave waveforms from Einstein Telescope using deep learning

arXiv

引用

arXiv 2024年

作者： Ma, CunLiang Yu, XinYao Cao, Zhoujian Jia, Mingzhen School of Information Engineering Jiangxi University of Science and Technology Ganzhou341000 China Jiangxi Provincial Key Laboratory of Multidimensional Intelligent Perception and Control Ganzhou341000 China Institute of Applied Mathematics Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Fundamental Physics and Mathematical Sciences Hangzhou Institute for Advanced Study UCAS Hangzhou310024 China

In the future, the third generation (3G) gravitational wave (GW) detectors, exemplified by the Einstein Telescope (ET), will be operational. The detection rate of GW from binary neutron star (BNS) is expected to reach approximately 104 per year. To address the challenges posed by BNS GW data processing for 3G GW detectors, this paper explores the extraction of BNS waveforms from ET. Drawing inspiration from SPIIR’s matched filtering approach, we introduce a novel framework leveraging deep learning for BNS waveform extraction. By integrating denoised outputs of time-delayed strain, we can reconstruct the embedded BNS waveform. We have established three distinct BNS GW denoising models, each tailored to address the early inspiral, later inspiral, and merger phases of BNS GW, respectively. To further regulate the waveform shape, we propose the Amplitude Regularity Model that takes denoised output as input and regulated waveform as output. The experiments conducted on test data demonstrate the efficacy of the denoising models, the Amplitude Regularity Models, as well as the overall waveform construction method. To the best of our knowledge, this marks the first instance of deep learning being utilized for the task of BNS waveform extraction. We believe that the proposed method holds promise for early warning, searching, and localization of BNS GWs. Copyright © 2024, The Authors. All rights reserved.

关键词： Gravity waves

来源：评论

学校读者我要写书评

暂无评论

A Large-Scale Plant in Treating Young Landfill Leachate: Focusing on the Application of Dopamine Modified Ceramic Membrane Smbr and Change of Nitrifying and Denitrifying Bacteria

SSRN

引用

SSRN 2024年

作者： Wang, Cong Wu, Hanyang Han, Runlin Zhang, Shujun Ni, Shou-Qing Li, Weicheng Qi, Weikang Peng, Yongzhen National Engineering Laboratory for Advanced Municipal Wastewater Treatment and Reuse Technology Beijing University of Technology Beijing100124 China Jiangxi Bocent Advanced Ceramic Environmental Technology Co. Ltd Jiangxi Province Pingxiang337000 China School of Chemistry and Chemical Engineering Jinggangshan University Ji'an343009 China Beijing Drainage Group Co. Ltd. Beijing100044 China Shandong Key Laboratory of Environmental Processes and Health Shandong Provincial Key Laboratory of Water Pollution Control and Resource Reuse School of Environmental Science and Engineering Shandong University Shandong Qingdao266237 China

A combined DAF-UASB-A/O system plus ceramic membrane submerged MBR (SMBR) was conducted in treating young landfill leachate with a capacity of 300 m3 /day. The degradation efficiency in biological unit and membrane filtration within 185 days of operation was investigated partly for the pollutant’s abatement and partly for high quality of permeate for UF/RO treatment as final tertiary treatment. A substantial reduced number of COD (96.8%), ammonia (83.4%) and TN (79.6%) was gained via DAF-UASB -A/O system. The dopamine modified ceramic membrane can enable SMBR to operate average 28 LMH because of good surface hydrophilicity. The dopamine modified ceramic membrane SMBR was fouling-free MBRs because immersed chemical cleaning can recover the filterability of ceramic membrane. Interestingly, enrichment of some species with the prolong of the successive anaerobic-anoxic-aerobic treatment system was companied with severe or slight suppression of other species via comparative study in sludge Day-1 and Day-185, respectively. Regarding the nitrifying and denitrifying microorganisms, simultaneous methanogensis and denitrification (SMD) process can be confirmed in the UASB because of the Thauera identified. The presence of Planctomycetes suggested anaerobic ammonium oxidation (ANAMMOX) process happened in the A/O tank. © 2024, The Authors. All rights reserved.

关键词： Ammonia

来源：评论

学校读者我要写书评

暂无评论

Hydrodynamics of heterogeneous particle swarms in gas-liquid-solid stirred tanks with free surface studied by DEM-VOF

引用

Powder Technology 2025年 462卷

作者： E, Dianyu Wen, Yingming Wu, Yiliu Li, Jing Sun, Weifu Liu, Yonggang Chen, Chao Cui, Jiaxin Qiu, Yinxuan Jiangxi Provincial Key Laboratory of Particle Technology Jiangxi University of Science and Technology Nanchang330013 China Research Centre for Intelligent Mineral Processing & Metallurgy International Institute for Innovation Jiangxi University of Science and Technology Nanchang330013 China ARC Research Hub for Smart Process Design and Control Department of Chemical and Biological Engineering Monash University VIC3800 Australia School of Civil Engineering Southeast University Nanjing211189 China Advanced Technology Research Institute Beijing Institute of Technology Jinan250307 China Faculty of Materials Metallurgy and Chemistry Jiangxi University of Science and Technology Ganzhou341000 China Xinyu Iron and Steel Group Co. Ltd Xinyu338001 China College of Materials Science and Engineering Taiyuan University of Technology Taiyuan030024 China School of Chemical Engineering The University of Queensland QLD4072 Australia

Stirred tanks play a pivotal role in material blending, yet there is still a lack of data on inner complex blending mechanism, especially involving heterogeneous particle swarm with different shape combinations. This study extensively develops the DEM-VOF model, in which different drag force models are applied based on the identification of particle shapes. It aims to provide an insight into the blending system dynamics of heterogeneous particle swarms through seven arranged particle schemes building upon three different shapes. The results show that the oblate-prolate ellipsoid particle scheme has the optimal suspension performance, but demonstrates high power consumption. The addition of spherical particles into the oblate-prolate ellipsoid particle scheme has significant effect on particle suspension, fluid flow and the evolution of free surface, while contributes to a reduction in power consumption. This study presents an analysis of the hydrodynamics and optimization of blending systems involving heterogeneous particle swarms in stirred tanks. © 2025 Elsevier B.V.

关键词： Flow of gases

来源：评论

学校读者我要写书评

暂无评论

Asymptotic state estimation for linear systems with sensor and actuator faults

引用

Science China(Information Sciences) 2019年第11期62卷 106-120页

作者： Chun-Hua XIE Hui YANG Dianhui WANG Zhe LI School of Electrical and Automation Engineering East China Jiaotong University Key Laboratory of Advanced Control & Optimization of Jiangxi Province Department of Computer Science and Information Technology La Trobe University College of Electrical and Information Engineering Hunan University

This paper investigates the asymptotic state estimation problem for linear systems with sensor and actuator faults, where the faults are modeled via multiple modes. For the case of sensor faults, we first introduce a new notion of detectability, i.e., detectability of system against sensor faults. The notion helps to address the question of whether it is possible to asymptotically estimate the system state by using the control input and system output, irrespective of which mode the system is in and what values the fault signals are.A necessary and sufficient condition for the system to be detectable against sensor faults is given, and then two switched observers are proposed for asymptotic state estimation with the help of maximin strategy. For the system with ? fault modes, we provide the explicit form of the switched observer, which is based on a bank of ?(?+1)/2 Luenberger-like or sliding-mode observers. Furthermore, extensions to the case of sensor and actuator faults are further studied. Finally, a simulation example of a reduced-order aircraft system is provided to show the effectiveness of the proposed approaches.

关键词： asymptotic state estimation observer linear systems sensor faults actuator faults

来源：评论

学校读者我要写书评

暂无评论

Prediction of component content in rare earth extraction process based on ESNs-Adaboost

引用

IFAC-PapersOnLine 2018年第21期51卷 42-47页

作者： Hui, Yang Shuli, Liu Rongxiu, Lu Jianyong, Zhu College of electrical and automation engineering East China Jiaotong University Nanchang China Key Laboratory for advanced control and optimization of Jiangxi Province Nanchang China

Aiming at the problem that the component content of elements in REEP (rare earth extraction process) is difficult to detect, a modeling method for component content in REEP based on ESNs-Adaboost is proposed. It uses ESN (echo state network) with fast training speed and high stability to establish multiple identification model of REEP. And then combined with the improved Adaboost algorithm, multiple models are integrated into a final ESNs-Adaboost model of component content of REEP according to the combination rule. The improved Adaboost algorithm has an advantage that its threshold can be adjusted adaptively with the training error. By collecting data from CePr/Nd extraction process, the simulation results show that the proposed method has high prediction accuracy, strong generalization ability, good robustness and better performance than single ESN model, which can meet the requirements of rapid detection of component content in extraction field. © 2018

关键词： Adaptive boosting Extraction Forecasting Rare earths Echo state networks Extraction process Generalization ability Model prediction Multiple identifications Prediction accuracy Rare earth extraction Rare earth extraction process

来源：评论

学校读者我要写书评

暂无评论

Time-delay assisted mechanism and adaptive LSTM hybrid train braking model of heavy haul trains

引用

control Engineering Practice 2025年 163卷

作者： Qiang Liu Kexuan Xu Yating Fu Jiang Liu Ling Liu State Key Laboratory of Synthetical Automation for Process Industries (Northeastern University) Shenyang 110819 China Key Laboratory of Advanced Control and Optimization of Jiangxi Province Nanchang 330013 China School of Electrical and Automation Engineering East China Jiaotong University Nanchang 330013 China Frontiers Science Center for Smart High-speed Railway System Beijing 100044 China School of Electronic and Information Engineering Beijing Jiaotong University Beijing 100044 China Beijing National Railway Research & Design Institute of Signal Communication Group Company Beijing 100070 China

The train braking model (TBM) that describes the dynamic relations of operation speed, mileage, and control force is essential for achieving stable operation and precise stopping of heavy haul trains (HHTs). However, it is difficult to establish the TBM of HHTs due to complex characteristics: (i) the long body and air braking process of the HHTs may lead to unexpected time-delays of control force; and (ii) there are significant unmodeled dynamics caused by rough tracks and external poor environment. Traditional TBM does not take into account the unmodeled dynamics and time-delays caused by air transmission during braking. To address these issues, this study proposes a data mechanism hybrid modeling strategy, which incorporates a braking time-delay assisted mechanism model and an adaptive long and short-term memory (LSTM) model. A new Bayesian optimization based time-delay estimation method is first proposed to determine unknown time-delays of each carriage and the estimated time-delays are incorporated to generate the multi-point-mass kinetic mechanism model. Moreover, the error of the mechanism-driven model is adaptively compensated by a sliding window LSTM model to conduct the unmodeled dynamics. The effectiveness of the proposed method is demonstrated using the field data.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Color Constancy of Rare-Earth Solution Image with Adaptive Parameter

引用

IFAC-PapersOnLine 2018年第21期51卷 295-300页

作者： Yang, Hui Zhang, Xuqian Liu, Zhixin Lu, Rongxiu Tan, Chang Zhu, Jianyong College of Electrical and Automation Engineering East China Jiaotong University Nanchang Jiangxi330013 China Key Laboratory of Advanced Control and Optimization of Jiangxi Province NanchangJiangxi330013 China Jiangxi Golden Century Advanced Materials Co. Ltd NanchangJiangxi330013 China

It is known that the color of the rare earth solution image varies with the illumination condition due to the rugged environment of rare earth extraction industry and attenuation of lights, which makes soft-sensing model for rare earth element content based on the color characteristics of the rare earth solution have larger error. An unsupervised color correction algorithm with adaptive optimization of parameters for rare earth solution is proposed in this paper. Such an algorithm can realize proper color correction of rare earth solution by using Genetic Algorithm (GA) based Grey Edge algorithm. The GA based Grey Edge algorithm can handle the issue of parameter adaptive optimization caused by the rare earth solution change with the different illumination and extraction tank, and reduce the error between the designated area of the image and the designated area of the standard image. The simulation shows the effectiveness of the proposed algorithm. © 2018

关键词： Edge detection Color Color computer graphics Extraction Gallium Genetic algorithms Image processing Parameter estimation Rare earths adaptive Adaptive optimization Color characteristics Color constancy Grey Edge Illumination conditions Rare earth extraction Rugged environments

来源：评论

学校读者我要写书评

暂无评论

Ternary Compression for Communication-Efficient Federated Learning

arXiv

引用

arXiv 2020年

作者： Xu, Jinjin Du, Wenli Jin, Yaochu He, Wangli Cheng, Ran The Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai200237 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China The Department of Computer Science University of Surrey GuildfordGU2 7XH United Kingdom Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Learning over massive data stored in different locations is essential in many real-world applications. However, sharing data is full of challenges due to the increasing demands of privacy and security with the growing use of smart mobile devices and IoT devices. Federated learning provides a potential solution to privacy-preserving and secure machine learning, by means of jointly training a global model without uploading data distributed on multiple devices to a central server. However, most existing work on federated learning adopts machine learning models with full-precision weights, and almost all these models contain a large number of redundant parameters that do not need to be transmitted to the server, consuming an excessive amount of communication costs. To address this issue, we propose a federated trained ternary quantization (FTTQ) algorithm, which optimizes the quantized networks on the clients through a self-learning quantization factor. Theoretical proofs of the convergence of quantization factors, unbiasedness of FTTQ, as well as a reduced weight divergence are given. On the basis of FTTQ, we propose a ternary federated averaging protocol (T-FedAvg) to reduce the upstream and downstream communication of federated learning systems. Empirical experiments are conducted to train widely used deep learning models on publicly available datasets, and our results demonstrate that the proposed T-FedAvg is effective in reducing communication costs and can even achieve slightly better performance on non-IID data in contrast to the canonical federated learning algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Learning algorithms

来源：评论

学校读者我要写书评

暂无评论

A Neuron-Significance-Index-based Self-organization Pruning Algorithm for S-LINN

引用

IFAC-PapersOnLine 2017年第1期50卷 14976-14981页

作者： Yang, Gang Wang, Le Chen, Wen Dai, Lizhen Yang, Hui School of Electrical and Automation Engineering East China Jiaotong University Key Laboratory of Advanced Control and Optimization of Jiangxi Province Nanchang330013 China Division of Engineering Technology Wayne State University DetroitMI48202 United States

For constructing a span-lateral inhibition neural network (S-LINN) with optimal architecture and parameters for actual application, an improved self-organizing optimization approach is proposed in this paper to tune the architecture and parameters simultaneously. This self-organization pruning algorithm can prune insignificant hidden neurons automatically through building a modified significance index function to evaluate the significance of hidden neurons. A preprocessing training of the initial network with assumed redundant hidden neurons will be allowed in the tuning process. A subsequent learning after the self-organization pruning process is also implemented to optimize the parameters of pruned network. The proposed self-organizing approach has been tested on nonlinear dynamic system identification benchmark problem. Simulation results demonstrate that the proposed method has good exploration and exploitation capabilities in terms of searching the optimal structure and parameters for S-LINN. © 2017

关键词： Neural networks Dynamical systems Network architecture Neurons Nonlinear dynamical systems Structural optimization Exploration and exploitation Lateral inhibition Optimal architecture Pruning Redundant hidden neurons Self organizations Self organizing approaches Self organizing optimization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：