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学者姓名：乔俊飞
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Abstract ：
In many fields, spatiotemporal prediction is gaining more and more attention, e.g., air pollution, weather forecasting, and traffic forecasting. Water quality prediction is a spatiotemporal prediction task. However, there are several challenges in water quality prediction: 1) Water quality time series has a complex nonlinear relationship, making it difficult to predict; 2) Water quality sensors are distributed on the river networks and have a strong spatial dependence on water quality prediction; and 3) Poor long-term forecast accuracy. To solve these problems, this work proposes a spatiotemporal prediction model called a Fusion Spatio-temporal Graph Convolution Neural network (FSGCN). First, This work uses a temporal attention mechanism to solve the nonlinear problem of water quality time series. Second, It adopts a graph convolution to extract spatial dependencies of river networks, and the fusion of spatiotemporal can more easily capture spatiotemporal features. Third, it adopts a temporal convolution residual mechanism, improving long-term series prediction accuracy. This work adopts two real-world datasets to evaluate the proposed FSGCN, and experiments demonstrate that FSGCN outperforms several state-of-the-art methods in terms of prediction accuracy.
Keyword ：
river network river network graph convolution neural network graph convolution neural network spatiotemporal fusion spatiotemporal fusion temporal convolution residual temporal convolution residual Water quality prediction Water quality prediction
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| GB/T 7714 | Qiao, Junfei , Lin, Yongze , Bi, Jing et al. Attention-Based Spatiotemporal Graph Fusion Convolution Networks for Water Quality Prediction [J]. | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING , 2024 . |
| MLA | Qiao, Junfei et al. "Attention-Based Spatiotemporal Graph Fusion Convolution Networks for Water Quality Prediction" . | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (2024) . |
| APA | Qiao, Junfei , Lin, Yongze , Bi, Jing , Yuan, Haitao , Wang, Gongming , Zhou, Mengchu . Attention-Based Spatiotemporal Graph Fusion Convolution Networks for Water Quality Prediction . | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING , 2024 . |
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Abstract ：
Under intermittent noncollocated boundary measurement (BM), this article introduces a dynamic intermittent boundary output-feedback control for reaction-diffusion systems (RDSs). Since the system state is not fully available and intermittent noncollocated BM makes the intermittent BC design very difficult, an observer-based control technique is given to surmount this design difficulty. Initially, a PDE state observer under intermittent noncollocated BM is provided to estimate the RDS state. Then, the exponential stability of closed-loop RDSs is ensured by constructing an observer-based controller. Sufficient conditions of such dynamic controller are subsequently presented by linear matrix inequalities (LMIs) via employing a switching time-dependent LF and inequality techniques. Finally, two numerical examples are presented to demonstrate the effectiveness of the proposed design approach.
Keyword ：
reaction-diffusion systems (RDSs) reaction-diffusion systems (RDSs) Dynamic intermittent boundary control (DIBC) Dynamic intermittent boundary control (DIBC) intermittent noncollocated boundary measurement (BM) intermittent noncollocated boundary measurement (BM)
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| GB/T 7714 | Wang, Zi-Peng , Zhao, Feng-Liang , Qiao, Junfei et al. Dynamic Intermittent Boundary Control for Reaction-Diffusion Systems Under Intermittent Noncollocated Boundary Measurement [J]. | IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS , 2024 . |
| MLA | Wang, Zi-Peng et al. "Dynamic Intermittent Boundary Control for Reaction-Diffusion Systems Under Intermittent Noncollocated Boundary Measurement" . | IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS (2024) . |
| APA | Wang, Zi-Peng , Zhao, Feng-Liang , Qiao, Junfei , Wu, Huai-Ning , Huang, Tingwen . Dynamic Intermittent Boundary Control for Reaction-Diffusion Systems Under Intermittent Noncollocated Boundary Measurement . | IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS , 2024 . |
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Abstract ：
The rise of edge intelligence is driving a shift in the focus of complexity computing to the edge. Due to network and communication constraints, traditional edge computing resource scheduling solutions for industrial Internet of Thing (IIoT) usually face many challenges. For example, delayed decision release, unreasonable policy scheduling and under-utilization of resources. These problems hinder the further construction and advancement of intelligent IIoT. In order to solve these problems, this paper proposes an edge computing resource scheduling scheme based on collective learning. The process of model training is formulated as a Markovian decision process (MDP). The scheme enables edge nodes to exchange learning experiences of resource scheduling schemes, through a shared ledger on the blockchain, including parameters for initial model training. The updated policy scheduling scheme is then obtained through a collective deep reinforcement learning (CDRL) algorithm. Also, to reduce the transmission burden of the underlying industrial devices, we benefit ambient backscatter communication (AmBC) to improve the power utilization of battery. Simulation results display our proposed scheme can reduce energy consumption significantly, while decreased approximately 12.6% compare to A3C algorithm.
Keyword ：
collective deep reinforcement learning (CDRL) collective deep reinforcement learning (CDRL) blockchain blockchain ambient backscatter communication (AmBC) ambient backscatter communication (AmBC) industrial Internet of Things (IIoT) industrial Internet of Things (IIoT) Mobile edge computing (MEC) Mobile edge computing (MEC)
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| GB/T 7714 | Huang, Yudian , Li, Meng , Yu, F. Richard et al. Resources Scheduling for Ambient Backscatter Communication-Based Intelligent IIoT: A Collective Deep Reinforcement Learning Method [J]. | IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING , 2024 , 10 (2) : 634-648 . |
| MLA | Huang, Yudian et al. "Resources Scheduling for Ambient Backscatter Communication-Based Intelligent IIoT: A Collective Deep Reinforcement Learning Method" . | IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 10 . 2 (2024) : 634-648 . |
| APA | Huang, Yudian , Li, Meng , Yu, F. Richard , Si, Pengbo , Zhang, Haijun , Qiao, Junfei . Resources Scheduling for Ambient Backscatter Communication-Based Intelligent IIoT: A Collective Deep Reinforcement Learning Method . | IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING , 2024 , 10 (2) , 634-648 . |
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Abstract ：
The real-time detection technique and comprehensive characterization of dioxin (DXN) emission concentration during the municipal solid waste incineration process persist as unresolved challenges. Prevailing research predominantly relies on data-driven models, often overlooking the potential benefits derived from fusing combustion mechanism knowledge. To confront this issue, we propose a hybrid modeling strategy that fuses a simulator-based mechanism model with an enhanced regression decision tree-based data model. This approach aims to predict DXN emission concentrations while accommodating diverse time-scaled measurement requirements. Based on virtual mechanism data obtained via numerical simulation models coupling FLIC and Aspen Plus, we constructed a white-box surrogate model utilizing a multiple-input multiple-output linear regression decision tree (LRDT). To establish a relationship with DXN emission concentration, we employed a semisupervised transfer learning mapping model. It was then fused with a novel ensemble LRDT model based on real historical data by using a constrained incremental random weight neural network. The efficacy of this modeling strategy was validated through an industrial application case study conducted in Beijing.
Keyword ：
Numerical models Numerical models municipal solid waste incineration (MSWI) municipal solid waste incineration (MSWI) MIMO communication MIMO communication Data models Data models Mathematical models Mathematical models multidemand modeling multidemand modeling Predictive models Predictive models Solid modeling Solid modeling Combustion Combustion linear regression decision tree (LRDT) linear regression decision tree (LRDT) mechanism-driven (MD) and data-driven (DD) mechanism-driven (MD) and data-driven (DD) numerical simulation numerical simulation Dioxin (DXN) Dioxin (DXN) semisupervised transfer learning semisupervised transfer learning
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| GB/T 7714 | Xia, Heng , Tang, Jian , Yu, Wen et al. Hybrid Simulator-Based Mechanism and Data-Driven for Multidemand Dioxin Emissions Intelligent Prediction in the MSWI Process [J]. | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS , 2024 . |
| MLA | Xia, Heng et al. "Hybrid Simulator-Based Mechanism and Data-Driven for Multidemand Dioxin Emissions Intelligent Prediction in the MSWI Process" . | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2024) . |
| APA | Xia, Heng , Tang, Jian , Yu, Wen , Qiao, Junfei . Hybrid Simulator-Based Mechanism and Data-Driven for Multidemand Dioxin Emissions Intelligent Prediction in the MSWI Process . | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS , 2024 . |
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Abstract ：
Coastal water quality prediction is the indispensable work to prevent the red tide and marine pollution accidents, which also provides the effective assistance to study ocean carbon sink. Due to the multiple inducing factors and their spatiotemporal coupling effects, the water quality prediction not only needs to be supported by big data, but also needs an effective model for prediction and analysis. However, most of the existing models frequently use timeline data from the same section or local collection point and cannot realize inversion and traceability of inducing factors. In this article, we consider these tough problems and propose an effective neurodynamics-driven prediction model for state evolution of coastal water quality (NDPM-CWQ). First, an event-driven deep belief network (EDBN) is designed and trained using the spatiotemporal data. Second, through the sensitivity analysis of the input variables in EDBN model, we rank influence degrees of spatiotemporal variables on the water quality and give the inversion and traceability of inducing factors. Third, the convergence of training EDBN is analyzed from the perspective of the stationary distribution and decision stability of Markov chain. Finally, the practical data-based experimental results show that the proposed NDPM-CWQ not only achieves better prediction performance, but also can quantitatively analyze the inversion and traceability of inducing factors.
Keyword ：
Predictive models Predictive models Analytical models Analytical models Couplings Couplings Data models Data models Training Training Sea measurements Sea measurements neurodynamics analysis neurodynamics analysis event-driven learning event-driven learning Biological system modeling Biological system modeling Coastal water quality prediction Coastal water quality prediction inversion and traceability of inducing factors inversion and traceability of inducing factors deep belief network deep belief network
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| GB/T 7714 | Wang, Gongming , Chen, Hong , Jiang, Suling et al. Neurodynamics-Driven Prediction Model for State Evolution of Coastal Water Quality [J]. | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2024 , 73 . |
| MLA | Wang, Gongming et al. "Neurodynamics-Driven Prediction Model for State Evolution of Coastal Water Quality" . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 73 (2024) . |
| APA | Wang, Gongming , Chen, Hong , Jiang, Suling , Han, Honggui , Qiao, Junfei . Neurodynamics-Driven Prediction Model for State Evolution of Coastal Water Quality . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2024 , 73 . |
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Abstract ：
Dioxin (DXN) is a persistent organic pollutant produced from municipal solid waste incineration (MSWI) processes. It is a crucial environmental indicator to minimize emission concentration by using optimization control, but it is difficult to monitor in real time. Aiming at online soft-sensing of DXN emission, a novel fuzzy tree broad learning system (FTBLS) is proposed, which includes offline training and online measurement. In the offline training part, weighted k-means is presented to construct a typical sample pool for reduced learning costs of offline and online phases. Moreover, the novel FTBLS, which contains a feature mapping layer, enhance layer, and increment layer, by replacing the fuzzy decision tree with neurons applied to construct the offline model. In the online measurement part, recursive principal component analysis is used to monitor the time-varying characteristic of the MSWI process. To measure DXN emission, offline FTBLS is reused for normal samples; for drift samples, fast incremental learning is used for online updates. A DXN data from the actual MSWI process is employed to prove the usefulness of FTBLS, where the RMSE of training and testing data are 0.0099 and 0.0216, respectively. This result shows that FTBLS can effectively realize DXN online prediction.
Keyword ：
Decision trees Decision trees municipal solid waste incineration (MSWI) municipal solid waste incineration (MSWI) online soft-sensing online soft-sensing time-varying time-varying Training Training Pollution measurement Pollution measurement Dioxin (DXN) Dioxin (DXN) Frequency modulation Frequency modulation Principal component analysis Principal component analysis fuzzy tree broad learning system (FTBLS) fuzzy tree broad learning system (FTBLS) Data models Data models Monitoring Monitoring
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| GB/T 7714 | Xia, Heng , Tang, Jian , Yu, Wen et al. Online Measurement of Dioxin Emission in Solid Waste Incineration Using Fuzzy Broad Learning [J]. | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2024 , 20 (1) : 358-368 . |
| MLA | Xia, Heng et al. "Online Measurement of Dioxin Emission in Solid Waste Incineration Using Fuzzy Broad Learning" . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 20 . 1 (2024) : 358-368 . |
| APA | Xia, Heng , Tang, Jian , Yu, Wen , Qiao, Junfei . Online Measurement of Dioxin Emission in Solid Waste Incineration Using Fuzzy Broad Learning . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2024 , 20 (1) , 358-368 . |
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Abstract ：
This article introduces a fuzzy boundary sampled-data (SD) control approach for a nonlinear distributed parameter system (DPS) with random time-varying delay, which belongs to two intervals and is considered by a probabilistic way to take the influence of uncertain factors, and boundary and distributed SD measurements are respected. Initially, this nonlinear DPS is represented precisely by a Takagi-Sugeno (T-S) fuzzy delayed partial differential equation (PDE) model. Subsequently, a fuzzy boundary SD control design is achieved under boundary and distributed SD measurements, employing linear matrix inequalities based on the T-S fuzzy delayed PDE model. This design ensures mean square exponential stability for the closed-loop delayed DPS through the use of inequality techniques and a Lyapunov functional. The membership functions of the proposed fuzzy boundary SD control law are independent of the fuzzy delayed PDE plant model and determined by the measurement output. Finally, the effectiveness of the designed fuzzy boundary SD controller is demonstrated via two simulation examples.
Keyword ：
Laboratories Laboratories distributed parameter system (DPS) distributed parameter system (DPS) Biological system modeling Biological system modeling Delays Delays Control design Control design Delay effects Delay effects boundary SD fuzzy control boundary SD fuzzy control Process control Process control random time-varying delays random time-varying delays Boundary and distributed sampled-data (SD) measurements Boundary and distributed sampled-data (SD) measurements Fuzzy control Fuzzy control
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| GB/T 7714 | Wang, Zi-Peng , Chen, Bo-Ming , Qiao, Junfei et al. Fuzzy Boundary Sampled-Data Control for Nonlinear DPSs With Random Time-Varying Delays [J]. | IEEE TRANSACTIONS ON FUZZY SYSTEMS , 2024 , 32 (10) : 5872-5885 . |
| MLA | Wang, Zi-Peng et al. "Fuzzy Boundary Sampled-Data Control for Nonlinear DPSs With Random Time-Varying Delays" . | IEEE TRANSACTIONS ON FUZZY SYSTEMS 32 . 10 (2024) : 5872-5885 . |
| APA | Wang, Zi-Peng , Chen, Bo-Ming , Qiao, Junfei , Wu, Huai-Ning , Huang, Tingwen . Fuzzy Boundary Sampled-Data Control for Nonlinear DPSs With Random Time-Varying Delays . | IEEE TRANSACTIONS ON FUZZY SYSTEMS , 2024 , 32 (10) , 5872-5885 . |
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Abstract ：
Broad learning system based on neural network (BLS-NN) has poor efficiency for small data modeling with various dimensions. Tree-based BLS (TBLS) is designed for small data modeling by introducing nondifferentiable modules and an ensemble strategy to the traditional broad learning system (BLS). TBLS replaces the neurons of BLS with the tree modules to map the input data. Moreover, we present three new TBLS variant methods and their incremental learning implementations, which are motivated by deep, broad, and ensemble learning. Their major distinction is reflected in the incremental learning strategies based on: 1) mean square error (mse); 2) pseudo-inverse; and 3) pseudo-inverse theory and stack representation. Therefore, this study further explores the domain of BLS based on the nondifferentiable modules. The simulations are compared with some state-of-the-art (SOTA) BLS-NN and tree methods under high-, medium-, and low-dimensional benchmark datasets. Results show that the proposed method outperforms the BLS-NN, and the modeling accuracy is remarkably improved with the small training data of the proposed TBLS.
Keyword ：
BLS-based on neural network (BLS-NN) BLS-based on neural network (BLS-NN) small data modeling small data modeling broad learning system (BLS) broad learning system (BLS) tree BLS (TBLS) tree BLS (TBLS)
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| GB/T 7714 | Xia, Heng , Tang, Jian , Yu, Wen et al. Tree Broad Learning System for Small Data Modeling [J]. | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2024 , 35 (7) : 8909-8923 . |
| MLA | Xia, Heng et al. "Tree Broad Learning System for Small Data Modeling" . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 35 . 7 (2024) : 8909-8923 . |
| APA | Xia, Heng , Tang, Jian , Yu, Wen , Qiao, Junfei . Tree Broad Learning System for Small Data Modeling . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2024 , 35 (7) , 8909-8923 . |
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Abstract ：
For nonlinear distributed parameter systems (DPSs) with multiple delays, this study considers a fuzzy fault-tolerant boundary control (BC) with stochastic actuator failures under boundary measurement. First, we exactly represent the nonlinear DPS with multiple delays by the Takagi-Sugeno (T-S) fuzzy delayed partial differential equation (PDE). Next, on basis of T-S fuzzy delayed PDE model, a fuzzy fault-tolerant BC design with stochastic actuator failures under boundary measurement guaranteeing the stochastically exponential stability for closed-loop DPS with multiple delays is subsequently presented by linear matrix inequalities. Last, the effectiveness of the investigated fuzzy fault-tolerant BC strategy with stochastic actuator failures under boundary measurement is proposed via a simulation example.
Keyword ：
Actuators Actuators Linear matrix inequalities Linear matrix inequalities Delays Delays Fault tolerant systems Fault tolerant systems Fault tolerance Fault tolerance Boundary measurement Boundary measurement distributed parameter system (DPS) distributed parameter system (DPS) stochastic actuator failures stochastic actuator failures Biological system modeling Biological system modeling multiple delays multiple delays Stochastic processes Stochastic processes fuzzy fault-tolerant boundary control (BC) fuzzy fault-tolerant boundary control (BC)
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| GB/T 7714 | Wang, Zi-Peng , Zhang, Xu , Qiao, Junfei et al. Fuzzy Fault-Tolerant Boundary Control for Nonlinear DPSs With Multiple Delays and Stochastic Actuator Failures [J]. | IEEE TRANSACTIONS ON FUZZY SYSTEMS , 2024 , 32 (5) : 3121-3131 . |
| MLA | Wang, Zi-Peng et al. "Fuzzy Fault-Tolerant Boundary Control for Nonlinear DPSs With Multiple Delays and Stochastic Actuator Failures" . | IEEE TRANSACTIONS ON FUZZY SYSTEMS 32 . 5 (2024) : 3121-3131 . |
| APA | Wang, Zi-Peng , Zhang, Xu , Qiao, Junfei , Wu, Huai-Ning , Huang, Tingwen . Fuzzy Fault-Tolerant Boundary Control for Nonlinear DPSs With Multiple Delays and Stochastic Actuator Failures . | IEEE TRANSACTIONS ON FUZZY SYSTEMS , 2024 , 32 (5) , 3121-3131 . |
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Abstract ：
A mixed fuzzy intermittent control method based on boundary control under boundary measurement and distributed control under spatial local averaged measurements (SLAMs) is introduced for nonlinear ordinary differential equations (ODE)-partial differential equations(PDE) coupled systems in this article. To accurately characterize the nonlinear ODE-PDE coupled systems, a Takagi-Sugeno fuzzy model is first employed. Then, based on the fuzzy model, the switched Lyapunov function is proposed to design the mixed fuzzy intermittent controller under boundary measurement and SLAMs. Sufficient conditions on stability for the closed-loop coupled system are obtained via a set of space dependent linear matrix inequalities. The simulation results ultimately confirm the effectiveness of the proposed design approach in controlling hypersonic rocket car.
Keyword ：
space- dependent linear matrix inequalities (SDLMIs) space- dependent linear matrix inequalities (SDLMIs) Hypersonic rocket car (HRC) Hypersonic rocket car (HRC) mixed fuzzy intermittent control mixed fuzzy intermittent control nonlinear ordinary differential equations- partial differential equations (ODE-PDE) coupled systems nonlinear ordinary differential equations- partial differential equations (ODE-PDE) coupled systems
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| GB/T 7714 | Wang, Zi-Peng , Su, Hua-Ran , Shi, Xi-Dong et al. Mixed Fuzzy Intermittent Control for Nonlinear ODE-PDE Coupled Systems [J]. | IEEE TRANSACTIONS ON FUZZY SYSTEMS , 2024 , 32 (12) : 6658-6670 . |
| MLA | Wang, Zi-Peng et al. "Mixed Fuzzy Intermittent Control for Nonlinear ODE-PDE Coupled Systems" . | IEEE TRANSACTIONS ON FUZZY SYSTEMS 32 . 12 (2024) : 6658-6670 . |
| APA | Wang, Zi-Peng , Su, Hua-Ran , Shi, Xi-Dong , Qiao, Junfei , Wu, Huai-Ning , Huang, Tingwen et al. Mixed Fuzzy Intermittent Control for Nonlinear ODE-PDE Coupled Systems . | IEEE TRANSACTIONS ON FUZZY SYSTEMS , 2024 , 32 (12) , 6658-6670 . |
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