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Chinese Control and Decision Conference, CCDC

作者： Shuhao Yang Jin Zhu Qin Wang Min Huang College of Information Science and Engineering State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang Liaoning China

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

The Forth Party Logistics (4PL) is becoming the main mode of logistics for its effective characteristics, of which the routing problem is the key problem. In order to find a suitable route, stochastic delay time must be considered. Routing problem with stochastic delay time is studied with the objective of minimize cost and the constraint of due date in this paper. First, both chance constrained programming model (CCPM) and expectation model (EM) are established for the problem. Then numerical experiments are used for models comparison and problem analysis. The results show that the CCPM is more corresponding in the face of stochastic delays, so that the optimal route of decision-making is more likely to meet the needs of routing problems in real life.

关键词： Delays Transportation Logistics Stochastic processes Numerical models Routing Programming

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An Energy Consumption Prediction LSTM Model of Metallurgy Enterprises 4

An Energy Consumption Prediction LSTM Model of Metallurgy En...

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4th International Conference on Environmental and Energy engineering, IC3E 2020

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

Aiming at the characteristics of multi-dimensional production data, complicated sources and diverse data structures of metallurgy enterprises, it is of great significance to study how to use energy management-related data to predict metallurgy enterprises' energy consumption. Using with an accurate measurement method, the enterprises can not only reduce the cost of energy consumption but also develop economic efficiency in producing for metallurgy enterprises, which results in low energy use efficiency, high enterprise cost, and weak scalability. In this paper, we establish an LSTM model to achieve energy consumption prediction for metallurgy enterprises by optimizing the model's parameters using a grid search algorithm. We also compare our model's prediction results with other mainstream machine learning algorithms, i.e., MARS and SVM through indexes such as MSE, RMSE, MAPE, and MRAE to evaluate the prediction effect of the learning algorithm. According to our simulation, LSTM performs best in the task of energy consumption prediction. © Published under licence by IOP Publishing Ltd.

关键词： Energy utilization

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The Optimization Model of 4PL Routing Problem for Risk-averse Customer

The Optimization Model of 4PL Routing Problem for Risk-avers...

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第32届中国控制与决策会议

作者： Fengxia Wang Min Huang Xingwei Wang College of Information Science and Engineering State Key Laboratory of Synthetical Automation for Process IndustriesNortheastern University College of Computer Science and Engineering Northeastern University

In order to improve the customer’s satisfaction of logistics transportation under uncertain environment,this paper studies the 4 PL routing optimization problem considering customer risk-aversion *** using the second-order stochastic dominance theory to constrain the optimal route and characterize the customer’s risk attitude,to minimize the total cost,a mathematical model considering the risk-aversion behavior of customers is *** optimization model was solved by CPLEX optimization ***,the effectiveness of the model is verified by simulation *** results show that the model considering customer risk-averse behavior can obtain a route with lower risk than the current route.

关键词： 4PL routing optimization risk-aversion second-order stochastic dominance

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Asymptotic Stability Analysis for Switched Systems under Spatio-temporal Specifications

Asymptotic Stability Analysis for Switched Systems under Spa...

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第40届中国控制会议

作者： Qian Liu Lijun Long Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing Institute of Artificial Intelligence Faculty of Information TechnologyBeijing University of Technology College of Information Science and Engineering Northeastern University China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University

This paper provides some sufficient conditions to achieve asymptotic stability of switched systems under spatiotemporal specifications via a state-dependent switching law. A common finite-time barrier function, multiple barrier functions and multiple Lyapunov functions are merged as a tool for analyzing the asymptotic stability of a switched nonlinear system under spatio-temporal specifications. Finally, a numerical example is presented to demonstrate the effectiveness of our proposed method.

关键词： Switched systems Common finite-time barrier function Multiple barrier functions Multiple Lyapunov functions State-dependent switching law

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Efficient Ensemble Broad Learning System Based on Dropout and Dropconnect

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The International Journal of Intelligent Control and Systems 2024年第2期29卷 79-87页

作者： Yiwan Cao Fei Chu Shuai Li C.L.Philip Chen Jun Fu School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China School of Computer Science and Engineering South China University of TechnologyGuangzhou 510006China State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110004China

Broad learning system(BLS)is an emerging neural network characterized by its rapid processing and robust generalization ***,determining the appropriate structure for broad learning system is also a *** addition,broad learning system may perform overfitting due to the dependence between nodes in processing fully connected *** deal with these problems,an efficient ensemble broad learning system based on Dropout and Dropconnect is proposed in this *** proposed Dropout ensemble broad learning system randomly discards hidden nodes to improve diversity between individuals and reduce the synergy between nodes to improve prediction *** Dropconnect ensemble broad learning system randomly drops connection weights to generate more complementary models by adding input attribute *** experimental results on the UCI datasets confirm that the method proposed in this paper can solve the problem of model overfitting caused by the strong dependence between the nodes of ensemble broad learning *** proposed algorithm outperforms the original BLS in terms of prediction stability and classification accuracy.

关键词： Broad learning system ensemble learning Dropout Dropconnect

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Parallel Temporal and Spatial Modeling for Interpretable Fault Detection and Isolation of Industrial processes

Parallel Temporal and Spatial Modeling for Interpretable Fau...

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International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Pengyu Song Chunhui Zhao Jinliang Ding Youxian Sun Xuanxuan Jin State Key Laboratory of Industrial Control Technology (College of Control Science and Engineering) Zhejiang University Hangzhou China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China Zhejiang Zheneng Jiahua Power Generator Co. Ltd. Hangzhou China

ISBN: (纸本)9781665402460

The structure of modern industrial processes has been gradually complicated to adapt to diversified production requirements. process variables generally have temporal characteristics. Meanwhile, complex spatial interactions between variables also pose challenges for process modeling. In this study, a parallel temporal and spatial feature extraction framework is proposed and applied to fault detection and isolation in industrial processes. On the one hand, unlike the existing methods with mixed spatiotemporal information, we design independent temporal and spatial modeling structures. The temporal characteristics of each process variable are simultaneously extracted in the designed temporal submodule. Furthermore, the spatial connections between variables are captured through sparse adjacency network extraction and information fusion. In this way, the temporal and spatial information can be individually observed to provide interpretable monitoring results. On the other hand, considering the different spatiotemporal anomalies caused by various fault types, we establish a targeted isolation strategy to provide reliable fault analysis. For temporal faults, the reconstruction error indicators are designed to quantify the abnormality of the variable. Moreover, a network reconstruction model is developed to measure the spatial structure deviation and locate the fault source. The performance of the proposed method is verified through a real industrial example.

关键词： Sensitivity process modeling Fault detection Computational modeling Production Feature extraction Real-time systems

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Fault detection for sector-bounded non-linear systems with servo inputs and sensor stuck faults

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Journal of Control and Decision 2019年第3期6卷 147-165页

作者： Ying Gu Xiao-Jian Li School of Science Dalian Jiaotong UniversityDalianChina College of Information Science and Engineering Northeastern UniversityShenyangChina State Key Laboratory of Synthetical Automation of Process Industries Northeastern UniversityShenyangChina

This paper addresses the fault detection(FD)problem for discretetime sector-bounded non-linear systems with finite-frequency servo *** non-linear systems are firstly modelled as *** main contributions are that a novel FD filter which combines the finite-frequency H∞and H−indices is *** from the existing methods,the proposed FD scheme guarantees that the generated residuals are robust against the servo inputs in fault-free case and sensitive to them in faulty ***,the small sensor stuck faults including outagefaults can be *** this class of systems,the existing finite-frequency FD filter design methods are invalid.A new lemma is developed to characterise the system performances in finite-frequency *** addition,sufficient conditions for the existence of such a FD filter are derived by introducing slack variable and linear matrix inequalities ***,an example is presented to demonstrate the method and its effectiveness.

关键词： Fault detection servo inputs sensor stuck faults sector-bounded nonlinearity finite-frequency domain

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Wireless-Friendly Window Position Optimization for RIS-Aided Outdoor-to-Indoor Networks based on Multi-Modal Large Language Model

arXiv

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arXiv 2024年

作者： Hou, Jinbo Qiu, Kehai Zhang, Zitian Yu, Yong Wang, Kezhi Capolongo, Stefano Zhang, Jiliang Li, Zeyang Zhang, Jie The Department of Electronic and Electrical Engineering the University of Sheffield Sheffield United Kingdom The Department of Computer Science and Technology University of Cambridge CambridgeCB3 0FD United Kingdom Brunel University London United Kingdom The School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou China The Design & Health Lab Department of Architecture Built Environment and Construction Engineering Politecnico di Milano Italy The Department of Computer Science Brunel University London Middlesex UxbridgeUB8 3PH United Kingdom The State Key Laboratory of Synthetical Automation for Process Industries The College of Information Science and Engineering Northeastern University Shenyang China The Centre for Wireless Innovation Queen’s University of Belfast BelfastBT3 9DT United Kingdom

This paper aims to simultaneously optimize indoor wireless and daylight performance by adjusting the positions of windows and the beam directions of window-deployed reconfigurable intelligent surfaces (RISs) for RIS-aided outdoor-to-indoor (O2I) networks utilizing large language models (LLM) as optimizers. Firstly, we illustrate the wireless and daylight system models of RIS-aided O2I networks and formulate a joint optimization problem to enhance both wireless traffic sum rate and daylight illumination performance. Then, we present a multi-modal LLM-based window optimization (LMWO) framework, accompanied by a prompt construction template to optimize the overall performance in a zero-shot fashion, functioning as both an architect and a wireless network planner. Finally, we analyze the optimization performance of the LMWO framework and the impact of the number of windows, room size, number of RIS units, and daylight factor. Numerical results demonstrate that our proposed LMWO framework can achieve outstanding optimization performance in terms of initial performance, convergence speed, final outcomes, and time complexity, compared with classic optimization methods. The building’s wireless performance can be significantly enhanced while ensuring indoor daylight performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Daylighting

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Soft Sensing Model of Underflow Concentration for Thickener process based on Data Reconciliation

Soft Sensing Model of Underflow Concentration for Thickener ...

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Chinese Control and Decision Conference, CCDC

作者： Hualu Zhang Fuli Wang ihua Zhang Dakuo He School of Information Science and Engineering Northeastern University Shenyang State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

Measured data in industrial production inevitably contain stochastic errors that are influenced by the environment and equipment, and the stochastic errors will reduce the accuracy of the measured data. Thickeners are widely used in mineral processing and sewage treatment, but the accuracy of the variables of the thickener process cannot meet the requirements, caused by the stochastic errors, and the underflow concentration cannot be real-time detected. So, pressure sensors were installed in the thickener to establish the soft sensing model of the underflow concentration, but the model accuracy is low owing to the stochastic errors of pressure values. This paper presents a soft sensing model of underflow concentration based on data reconciliation. The data reconciliation method is used to improve the accuracy of variables in thickener process, and then the soft sensing model of underflow concentration is established using the reconciled pressure. Applying the data of impact thickener process, the validity of the model is confirmed.

关键词： Data models Feeds Real-time systems Pressure sensors Stochastic processes Production

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Bayesian Network-based Technical Index Estimation for Industrial Flotation process under Incomplete Data

Bayesian Network-based Technical Index Estimation for Indust...

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Chinese Control and Decision Conference, CCDC

作者： Hao Yan Jin Zhu Fuli Wang Dakuo He Qingkai Wang College of Information Science and Engineering Northeastern University Shenyang China State Key Laboratory of Process Automation in Mining and Metallurgy Beijing China

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

Due to the lack of detection instruments or long measurement cycles in the industrial flotation process, accurate and real-time estimation of the technical index is of great significance for optimizing flotation performance and operational adjustment. In the real-world flotation process, incomplete data is a widespread phenomenon owing to hardware sensor failures and other reasons. To this end, this paper proposes a Bayesian network (BN)-based concentrate grade estimation method under incomplete data. The real-time froth image information and the concentrate grades of the previous periods are taken as the input of the BN model, and the current concentrate grade is the output of the BN model. The expectation maximum (EM) algorithm is used to estimate the model parameters. The application results show the proposed method can accurately estimate the concentrate grade even if some data are missing.

关键词： Minerals Real-time systems Indexes Maximum likelihood estimation Manufacturing processes Predictive models

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