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2021 International Conference on Smart Energy and Energy Internet of Things, SEEIoT 2021

作者： Yu, Zhuchao Chu, Gaixia Zhang, Yuanzheng Zhou, Siyuan He, Shaowei Li, Jing School of Business Administration Northeastern University Shenyang China State Key Laboratory of Process Automation in Mining and Metallurgy Beijing China College of Medicine and Biological Information Engineering Northeastern University Shenyang China Qinghai XKXX Information Technology Co. LTD Xining China

The energy consumption data of beneficiation and metallurgy enterprises has the characteristics of multi-dimensional and timing. An energy consumption data anomaly detection model based on transfer learning is proposed to help enterprises monitor the abnormal energy use. The combination of transfer learning and DTW algorithm is used to eliminate the adverse impact of timing on the model, and the ensemble learning is used to realize data detection. Finally, an efficient and accurate energy consumption data anomaly detection model is formed. The detection accuracy of the model is 91.6%, which can effectively meet the requirements of enterprise energy management. © Published under licence by IOP Publishing Ltd.

关键词： Concentration (process)

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Graph Embedded Stochastic Configuration Networks for Imbalanced Data Classification

Graph Embedded Stochastic Configuration Networks for Imbalan...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yuanhang Qiu Dianhui Wang Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This paper presents a novel approach called Graph Embedded Stochastic Configuration Networks (GESCNs) for complex data classification. To enhance the learning of geometric and discriminative features, especially in imbalanced and noisy datasets, the proposed GESCNs method integrates graph embedding strategies with Stochastic Configuration Networks (SCNs), a novel incremental randomized learning method known for its efficient model configuration mechanism. The intrinsic and penalty graph matrices help preserve topological relationships between data points, while SCNs ensure effective learning and strong generalization in modeling. Extensive experiments on diverse datasets demonstrate that GESCNs consistently outperform other randomized learning models across key metrics. These findings underscore the robustness of GESCNs in handling noisy and imbalanced data, making it a highly effective solution for challenging classification tasks.

关键词： Learning systems Uncertainty Scalability Stochastic processes Signal processing Data structures Data models Robustness Noise measurement Speech processing

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Numerical Modeling of Wet Milling process by an Improved CFD-DEM-VOF Model

MetaResource

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MetaResource 2025年第1期2卷 33-55页

作者： Huaqing Ma Changhua Xie Wenrui Wang Lianyong Zhou Zihan Liu Tao Song Guobin Zou Jiawei Yang Zongqing Zhou Kaiwei Chu Yongzhi Zhao School of Qilu Transportation Shandong University Jinan China Zhejiang Huayou Cobalt Co. Ltd Jiaxing China Institute of Process Equipment College of Energy Engineering Zhejiang University Hangzhou China State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process Beijing China

Wet ball mill that has been extensively used to comminute the raw materials in various industries possesses the advantages of large production capacity, high grinding efficiency, low investment and so on. Wet mill is a typical gas-liquid-solid three-phase system, and the interaction between these three phases is quite complex, resulting in the difficulty to simulate the wet milling process. As a consequence, a numerical model by coupling computational fluid dynamics (CFD), discrete element method (DEM) and volume of fluid (VOF) is developed to accurately simulate the wet milling process. A novel scheme is also devised and incorporated into CFD-DEM-VOF model to treat the interphase coupling when the particles and CFD cells have the comparable size that will be encountered in simulating the wet milling process. The accuracy of the established CFD-DEM-VOF coupling model is then validated by several test cases, including the single particle sedimentation in air-liquid domain, water entry of particle assembly and three-phase flows in a lab-scale wet mill and an industrial-scale wet mill. Furthermore, the effects of CFD cell size (2.5, 5 and 10 mm) and mill rotation speed (10, 30 and 50 r/min) on the prediction accuracy are also explored by the test case of lab-scale mill. The results indicate that using the quite coarse CFD cells can deteriorate the simulation accuracy, and increasing the mill rotation speed will enhance this influence. Nevertheless, adopting the very fine CFD cells (e.g., 2.5 mm in this work) in our model is not necessary in terms of the accuracy in simulating the particle behaviors, and the reliable prediction of particle behaviors can still be obtained while using the relatively large CFD cells (e.g., 5 mm in this paper).

关键词： Couplings Accuracy Fluids Computational fluid dynamics Atmospheric modeling Milling Raw materials Numerical models Sedimentation Assembly

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An Improved MOEA Based on Adaptive Adjustment Strategy for Optimizing Deep Model of RFID Indoor Positioning

An Improved MOEA Based on Adaptive Adjustment Strategy for O...

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International Conference on Computer Supported Cooperative Work in Design

作者： Jiahui Liu Lvqing Yang Sien Chen Wensheng Dong Bo Yu Qingkai Wang School of Informatics Xiamen University Xiamen China Tech Valley(Xiamen)Information Technology Co. Ltd. Xiamen China School of Navigation Jimei University Xiamen China Zijin Zhixin(Xiamen)Technology Co. Ltd. Xiamen China State Key Laboratory of Process Automation in Mining & Metallurgy Beijing China Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China

Nowadays, IoT technology is developing rapidly and RFID (Radio Frequency Identification) based indoor positioning problems can be performed using deep learning and intelligent optimization algorithms. Deep models can analyze and predict the localization problem as a regression problem to achieve high accuracy positioning. Meanwhile, to ensure the accuracy of the model, we need to find excellent hyperparameters, which requires the support of optimization algorithms, but existing optimization algorithms do not allow flexible adaptation according to the optimization phase and there is room for improvement. In this paper, we propose a deep model, called CTT, and a multi-objective evolutionary algorithm (MOEA) based on a neighborhood adaptive adjustment strategy, called MOEA-NAAS. The experimental results show that CTT optimized by the NAAS algorithm is significantly more accurate and stable in the localization problem, with significant improvements in the three main metrics, proving the usability of the optimization algorithm. At the same time, the localization effect of the CTT also shows obvious advantages. In the future, the optimized algorithm can be combined with other deep models and widely used in various high-precision indoor positioning.

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A Soft Sensor Model Based on HGA-LSTM for the Feed Density in a Thickener

A Soft Sensor Model Based on HGA-LSTM for the Feed Density i...

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第33届中国过程控制会议

作者： Kang Li Lianyu Wang Guobin Zou BGRIMM Technology Group State Key Laboratory of Process Automation in Mining and Metallurgy Research College of Information Science and Engineering Northeastern University

This study focused on the issue of accurately measuring the feed density in *** problem is common in the mineral processing ***,owing to the limitations of existing instruments and devices,the feed density in the thickener is difficult to measure online *** sensors are extensively utilized as an effective *** long short-term memory(LSTM) network has been proposed for the soft sensor model development and has shown significant modeling ability considering time ***,it is challenging to develop an accurate prediction model as it involves optimizing the hyperparameters,such as the time window size and structure of the LSTM ***,aiming at the problem of accurately measuring the feed density,we proposed a new soft sensor modeling method based on the LSTM and hybrid genetic algorithm(HGA).Specifically,the HGA refers to a novel optimization search algorithm model that can optimize the hyperparameters and improve the modeling performance of the ***,the proposed soft sensor modeling method was successfully applied to a real thickener case in *** soft sensor prediction results demonstrated that the hybrid model outperformed other models and satisfied the measurement accuracy requirements in the factory well.

关键词： soft sensor thickening process feed density long short-term memory hybrid genetic algorithm

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Spatio-temporal feature fusion model based on Attention mechanism for RFID indoor positioning

Spatio-temporal feature fusion model based on Attention mech...

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International Conference on Computer Supported Cooperative Work in Design

作者： Houjin Chen Lvqing Yang Mulan Yang Xuehan Hou Sien Chen Wensheng Dong Bo Yu Qingkai Wang School of Informatics Xiamen University Xiamen China School of Economics and Finance Xi’an Jiaotong University Xian China School of Software Xi’an Jiaotong University Xian China School of Navigation Jimei University Xiamen China School of Management Xiamen University Xiamen China Zijin Zhixin(Xiamen)Technology Co. Ltd. Xiamen China State Key Laboratory of Process Automation in Mining & Metallurgy Beijing China Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Amidst the rapid advancement of Internet of Things (IoT) technology, achieving precise indoor localization has emerged as a pivotal research area. Localization algorithms relying on Radio Frequency Identification (RFID) received signal strength indicator (RSSI) have gained widespread adoption in numerous indoor positioning systems due to their straightforward implementation and cost-effectiveness. However, in indoor settings, challenges like building obstructions and multipath effects often lead to signal reception failures by RFID antennas, consequently compromising the reliability of positioning outcomes. Recent research has approached indoor localization as a regression problem, employing deep learning models for analysis and prediction. But most current indoor localization models primarily focus on either spatial or temporal features within RSSI data, leading to suboptimal localization outcomes. To tackle these challenges, this paper proposes an enhanced methodology that leverages Generative Adversarial Networks (GAN) to impute missing RSSI data. Additionally, Convolutional Neural Networks (CNN) are utilized to extract spatial domain features, while Long Short-Term Memory Networks (LSTM) are employed for extracting temporal domain features. Ultimately, this paper designs a novel model, GCLA, which integrates an Attention mechanism with a location coding strategy to fuse features for precise location prediction. Experimental results show that the proposed GCLA model can obtain stable localization results after a short training on a small number of datasets.

关键词： Location awareness Training Attention mechanisms Predictive models Feature extraction Generative adversarial networks Convolutional neural networks

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Online Self-Learning Fuzzy Recurrent Stochastic Configuration Networks for Modeling Nonstationary Dynamics

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IEEE Transactions on Fuzzy Systems 2025年第6期33卷 1753-1766页

作者： Dang, Gang Wang, Dianhui Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Liaoning Shenyang110819 China China University of Mining and Technology Research Center for Stochastic Configuration Machines Jiangsu Xuzhou221116 China

Fuzzy recurrent stochastic configuration networks (FRSCNs) are a class of randomized neuro-fuzzy models that have shown success in modeling nonlinear dynamic systems. However, the data generated from the real-world industry process often exhibits nonstationary characteristics, which could lead to the resulting model with poor generalization performance. This paper presents an online self-learning FRSCN (OSL-FRSCN) for problem-resolving. OSL-FRSCNs integrate the self-organizing learning strategies into FRSCNs, maintaining the model's strong fuzzy inference capabilities while enhancing their continuous learning abilities for nonstationary data streams. The network parameters are updated online using the projection algorithm based on the newly arriving data streams. Moreover, the network structure can be dynamically adjusted in the light of the fuzzy recurrent stochastic configuration (FRSC) algorithm and an improved sensitivity analysis. Comprehensive comparisons over two nonlinear system identification tasks and two industrial applications are carried out. Numerical results clearly demonstrate that the proposed OSL-FRSCNs outperform other neuro-fuzzy and non-fuzzy models with sound generalization, verifying their effectiveness in modeling nonlinear systems with nonstationary dynamics. © 1993-2012 IEEE.

关键词： Sensitivity analysis

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Multivariate statistical modelling and monitoring for nonstationary industrial process with slow feature analysis and temporal working pattern partition

Multivariate statistical modelling and monitoring for nonsta...

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第33届中国控制与决策会议

作者： Xiaovu Zou Jie Pan China University of Mining and Technology State Key Laboratory of Process Automation in Mining & Metallurgy

Due to change of working condition,nonstationarity widely exists in modern industry,leading to time-variant statistical properties and frustrating the modelling techniques for stationary *** this research,slow feature analysis and working pattern partition based method is proposed for nonstationary industrial process *** feature analysis is employed as a filter to separate the signals with different ***,stationarity test is applied to the ordered slow ***,four categories of features can be obtained with specific *** them,the slow nonstationary features represent the inner change of working condition and thus can be adopted to partition the working condition into multiple patterns,***,the nonstationarity can be decomposed into piece-wise stationarity in the feature *** working pattern can then be modelled separately,using the normal data based on stationary ***,the proposed method is validated on a real copper flotation process.

关键词： process Modelling Nonstationary Slow Feature Analysis Working Pattern

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Stochastic Configuration Networks for Data Augmented Seismic Magnitude Prediction

Stochastic Configuration Networks for Data Augmented Seismic...

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Chinese automation Congress (CAC)

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Geophysical exploration yields a vast amount of monitoring data that accurately captures the geological dynamics of areas under investigation, providing a rich dataset for the analysis of seismic events. Despite a growing interest in utilizing monitoring data for event-centric information learning, challenges such as data imbalance significantly pose formidable obstacle to accurate information learning. In this study, a novel data augmentation method termed Adaptive Feature Expansion (AFE) is proposed to mitigate data imbalance. Additionally, an innovative randomized learning paradigm with incremental model configuration mechanism, Stochastic Configuration Networks (SCNs), is employed to learn seismic information and predict magnitude ranges. The experimental results indicate that AFE can effectively balance seismic data in the feature space and present excellent scalability. SCNs outperform traditional machine learning algorithms in terms of predictive performance. This work makes insightful contributions to geological hazard assessment.

关键词： Training Adaptation models Accuracy Computational modeling Stochastic processes Predictive models Data models Timing Monitoring Testing

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Deeper Insights into Learning Performance of Stochastic Configuration Networks

arXiv

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arXiv 2024年

作者： Yan, Xiufeng Wang, Dianhui Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou22116 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China

Stochastic Configuration Networks (SCNs) are a class of randomized neural networks that integrate randomized algorithms within an incremental learning framework. A defining feature of SCNs is the supervisory mechanism, which adaptively adjusts the distribution to generate effective random basis functions, thereby enabling error-free learning. In this paper, we present a comprehensive analysis of the impact of the supervisory mechanism on the learning performance of SCNs. Our findings reveal that the current SCN framework evaluates the effectiveness of each random basis function in reducing residual errors using a lower bound on its error reduction potential, which constrains SCNs' overall learning efficiency. Specifically, SCNs may fail to consistently select the most effective random candidate as the new basis function during each training iteration. To overcome this problem, we propose a novel method for evaluating the hidden layer's output matrix, supported by a new supervisory mechanism that accurately assesses the error reduction potential of random basis functions without requiring the computation of the Moore-Penrose inverse of the output matrix. This approach enhances the selection of basis functions, reducing computational complexity and improving the overall scalability and learning capabilities of SCNs. We introduce a Recursive Moore-Penrose Inverse-SCN (RMPI-SCN) training scheme based on the new supervisory mechanism and demonstrate its effectiveness through simulations over some benchmark datasets. Experiments show that RMPI-SCN outperforms the conventional SCN in terms of learning capability, underscoring its potential to advance the SCN framework for large-scale data modeling applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Function evaluation

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