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检索条件"机构=State Key Laboratory of Process Automation in Mining and Metallurgy Ganization"

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Abnormal Detection Model of Energy Consumption Data in Beneficiation and metallurgy Enterprises based on Transfer Learning

Abnormal Detection Model of Energy Consumption Data in Benef...

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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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Hybrid mean impact value with nonnegative garrote for input variable selection of multi-layer perceptron 20

Hybrid mean impact value with nonnegative garrote for input ...

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Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering

作者： Hongxun Wang Fengying Ma Kai Sun School of Electrical Engineering and Automation Qilu University of Technology (shandong Academy of sciences) Jinan China State Key Laboratory of Process Automation in Mining & Metallurgy ganization Beijing China Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China School of Electrical Engineering and Automation Qilu University of Technology (shandong Academy of sciences) Jinan China School of Electrical Engineering and Automation Qilu University of Technology (shandong Academy of sciences) Jinan China State Key Laboratory of Process Automation in Mining & Metallurgy ganization Beijing China Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China

ISBN: (纸本)9781450387811

In the paper, a new adaptive variable selection algorithm for nonlinear regression multi-layer perceptron (MLP) is proposed. The proposed algorithm applies the nonnegative garrote (NNG) to shrink the input weights of the trained MLP. An adaptive indicator is designed by introducing the mean impact value (MIV) algorithm, which can improve the coefficient shrinkage efficiency of the NNG algorithm. The proposed algorithm uses cross-validation and Bayesian information criterion to calculate the optimal shrinkage parameter. The performance of adaptive variable selection algorithm is demonstrated by an example of artificial dataset. Simulation results show that the algorithm has better model accuracy and simplicity than other state-of-art algorithms.

关键词： Variable selection Multi-layer perceptron Mean impact value Nonnegative garrote

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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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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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Research on soft sensor of copper concentrate grade based on NNG-LSSVM 20

Research on soft sensor of copper concentrate grade based on...

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Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering

作者： Hao Wu Jianpeng Sun Bingqing Pan Kai Sun School of Electrical Engineering and Automation Qilu University of Technology (shandong Academy of sciences) Jinan China School of Electrical Engineering and Automation Qilu University of Technology (shandong Academy of sciences) Jinan China State Key Laboratory of Process Automation in Mining & Metallurgy ganization Beijing China Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China

ISBN: (纸本)9781450387811

In the ore flotation process, the grade of copper concentrate determines the quality of the final product. Nevertheless, in the actual production, the measurement of this parameter takes a long time, making it difficult to measure in real time online. In this paper, a soft sensor method based on nonnegative garrote (NNG) and least squares support vector machine (LSSVM) is proposed. This paper uses the actual production data provided by the DCS system to predict and model this variable. The simulation results show that the researched soft-sensing method can accurately predict the change of copper concentrate grade, and can achieve real-time estimation during actual production process. In addition, the accuracy of the proposed algorithm is obviously superior to other soft-sensing methods.

关键词： nonnegative garrote soft sensor LSSVM copper flotation

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Collaboration Energy Efficiency with Mobile Edge Computing for Target Tracking in IoT 7th

Collaboration Energy Efficiency with Mobile Edge Computing f...

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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Zou, Guobin Yu, Gang Zhang, Jian Tang, Jian Zhou, Junwu College of Information Science and Engineering Northeastern University Shenyang110819 China State Key Laboratory of Process Automation in Mining and Metallurgy Beijing102600 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China Faculty of Information Technology Beijing University of Technology Beijing100024 China Beijing Key Laboratory of Process Automation in Mining and Metallurgy Beijing102600 China BGRIMM Technology Group Co. Ltd. Beijing102600 China

ISBN: (纸本)9783030786205

In this paper, the target tracking problem is investigated with mobile edge computing (MEC) mechanism in internet of things (IoT), where the challenge of energy efficiency is a significant issue when the target tracking event is driven. In order to prolong the lifetime of IoT, we adopt dynamic clustering methods to improve energy efficiency while guaranteeing target tracking effects. We deign the sensor selection scheme for carrying out the tracking tasks according to energy distribution of the sensor node. Concretely, by considering the reality of random deployment and introducing the definition of node density for IoT, we develop a Pareto optimality for sensor nodes selection in terms of energy efficiency without reducing the accuracy of target tracking. Furthermore, we recruit voluntary mobile devices as mobile edge computing servers to offload the data from selected sensor nodes in the cluster and process them to the estimate the target state. Simulations demonstrate the efficiency for tracking performance on energy balance in terms of efficiently prolonging the IoT lifetime. © 2021, Springer Nature Switzerland AG.

关键词： Target tracking

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Collaboration Energy Efficiency with Mobile Edge Computing for Data Collection in IoT 7th

Collaboration Energy Efficiency with Mobile Edge Computing f...

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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Zou, Guobin Zhang, Jian Tang, Jian Zhou, Junwu College of Information Science and Engineering Northeastern University Shenyang110819 China State Key Laboratory of Process Automation in Mining & Metallurgy Beijing102600 China School of Computer and Software Nanjing University of Information Science & Technology Nanjing210044 China Faculty of Information Technology Beijing University of Technology Beijing100024 China Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing102600 China BGRIMM Technology Group Co. Ltd. Beijing102600 China

ISBN: (纸本)9783030786144

In this paper, we investigated collaboration energy efficiency with mobile edge computing (MEC) mechanism in internet of things (IoT), which is a challenge issue. In order to prolong the lifetime of IoT, we adopt dynamic clustering methods to improve energy efficiency while guaranteeing energy balance. We deign the sensor selection scheme for collecting data according to Pareto optimality. Concretely, by considering the reality of random deployment and introducing the definition of node density for IoT, we develop a Pareto optimality for sensor nodes selection in terms of energy efficiency. Furthermore, we recruit voluntary mobile devices as mobile edge computing servers to offload the data from selected sensor nodes in the cluster and process them to the estimate the data state. Simulations demonstrate the efficiency for the performance on energy balance in terms of efficiently prolonging the IoT lifetime. © 2021, Springer Nature Switzerland AG.

关键词： Internet of things

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Grade Monitoring using Semantic Features of Flotation Froth Image

Grade Monitoring using Semantic Features of Flotation Froth ...

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Chinese Control and Decision Conference, CCDC

作者： Xu Wang Jingmin Lian Daoxi Liu Yutian Lei School of Information Science and Engineering Northeastern University Shenyang China BGRIMM Technology Group Beijing China State Key Laboratory of Process Automation in Mining & Metallurgy Beijing China

ISBN: (纸本)9781665478977

In the industrial flotation circuits, grades are the key performance indicators for flotation condition recognition and operations. Modeling based on machine vision is an effective tool for grade monitoring, and froth image feature extraction as a model input plays a crucial role. Given the low efficiency of hand-crafted features and the poor interpretability of high-dimensional features extracted by convolutional neural networks, a grade monitoring method with human-understandable semantic features as input is proposed in this paper. First, based on the combination of generative adversarial networks and encoder, we design a new network structure that maps the froth images data space to the latent semantic space. Then the semantic features of froth images can be automatically extracted by decomposing the constructed matrix with rich latent semantics. Finally, as demonstrated in the industrial experiment, the extracted semantic features can not only be visually interpreted but also can be effectively used in grade monitoring.

关键词： Machine vision Semantics key performance indicator process control Predictive models Feature extraction Generative adversarial networks

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