Deep Extreme Learning Machines Based Two-Phase Spatiotemporal Modeling for Distributed Parameter Systems
01 Mar 2023-IEEE Transactions on Industrial Informatics (IEEE Transactions on Industrial Informatics)-Vol. 19, Iss: 3, pp 2919-2929
TL;DR: In this article , a two-phase spatiotemporal (S/T) modeling framework based on deep extreme learning machine (DELM) is proposed for complex distributed parameter systems (DPSs).
Abstract: Accurate and robust modeling of complex distributed parameter systems (DPSs) is a challenge for three reasons: 1) they have infinite-dimensional characteristics; 2) they are time/space coupled; and 3) there are model uncertainties. In this article, a two-phase spatiotemporal (S/T) modeling framework based on deep extreme learning machine (DELM) is proposed for DPSs. The modeling process consists of two S/T models in two phases: Phase I: a DELM model and Phase II: a Karhunen–Loève (KL) based ELM (KL-ELM) model. In phase I, the DELM model is constructed by combing the multilayer ELM (ML-ELM), ELM, and kernel-based ELM (K-ELM) to approximate the dominant S/T dynamics of DPSs. Since DPSs have an infinite-dimensional characteristic that can hardly be handled directly, ML-ELM is first employed to transform the infinite-dimensional systems into finite-dimensional systems. Then, the ELM model is adopted to further approximate the finite-dimensional systems to ensure the model can predict future dynamic behavior. Finally, the K-ELM is used to reconstruct the infinite-dimensional systems, which can be considered as the inverse process of ML-ELM. Thus, the final DELM model can be used for prediction in both space and time directions. In phase II, a KL-ELM model is constructed to compensate for modeling errors caused by reconstruction error or unknown nonlinear dynamics. By integrating the obtained DELM and KL-ELM models, the proposed two-phase S/T model can be constructed. Experiments on a typical industrial thermal process verified that the proposed method may work better in complex DPSs.
TL;DR: In this article , the authors developed a novel V2G scheduling method for consuming local renewable energy in microgrids by using a mixed learning framework, where battery safeguard strategies are derived via an offline soft-run scheduling process, where V2Gs management is modeled as a constrained optimization problem based on estimated microgrid and GEVs states.
Abstract: The adoption of grid-connected electric vehicles (GEVs) brings a bright prospect for promoting renewable energy. An efficient vehicle-to-grid (V2G) scheduling scheme that can deal with renewable energy volatility and protect vehicle batteries from fast aging is indispensable to enable this benefit. This article develops a novel V2G scheduling method for consuming local renewable energy in microgrids by using a mixed learning framework. It is the first attempt to integrate battery protective targets in GEVs charging management in renewable energy systems. Battery safeguard strategies are derived via an offline soft-run scheduling process, where V2G management is modeled as a constrained optimization problem based on estimated microgrid and GEVs states. Meanwhile, an online V2G regulator is built to facilitate the real-time scheduling of GEVs' charging. The extreme learning machine (ELM) algorithm is used to train the established online regulator by learning rules from soft-run strategies. The online charging coordination of GEVs is realized by the ELM regulator based on real-time sampled microgrid frequency. The effectiveness of the developed models is verified on a U.K. microgrid with actual energy generation and consumption data. This article can effectively enable V2G to promote local renewable energy with battery aging mitigated, thus economically benefiting EV owns and microgrid operators, and facilitating decarbonization at low costs.
27 May 2023
TL;DR: In this paper , the authors provide a concise overview of battery management system features, including battery charging optimization, temperature control, and cell voltage balancing, and highlight the potential for further study in the area of electric vehicles.
Abstract: Researchers are becoming more interested in electric vehicle (EV) because it assist to minimise greenhouse impacts, reduce noise and air pollution, and provide freedom from fossil fuels. Electric vehicles depend on their batteries to safely supply the necessary power. The duration of time needed to charge the electric batteries is the biggest drawback of modern electric vehicles. Significant progress has been achieved in recent years to manage energy storage and speed up the charging process for electric vehicle batteries. In order to reduce energy consumption, boost system efficiency, lengthen battery life, and create a clean, efficient transportation system, it is crucial to build a battery management system that ensures long product life and a safe driving experience. This article attempts to provide a concise overview of various important battery management system features, including battery charging optimization, temperature control, and cell voltage balancing. The conclusion and recommendation of the article highlight the potential for further study in the area of electric vehicles.
TL;DR: In this paper , a spatio-temporal inference system is proposed to detect and locate thermal abnormalities of battery systems, which consists of three modules: spatiotemporal processing module, abnormality inference module, and spatial inference module.
Abstract: In this article, a spatio-temporal inference system is proposed to detect and locate thermal abnormalities of battery systems. The proposed spatio-temporal inference system consists of three modules: spatio-temporal processing module, abnormality inference module, and spatial inference module. Based on the distributed temperatures on the battery system, the monitoring statistic can be developed in the spatio-temporal processing module. The abnormality inference module is constructed to detect the abnormality based on the derived statistic index. Then, the spatial Bayes model is designed to estimate the abnormality location. The Bayes risk analysis indicates that the proposed method has a bounded error. Experiments on a lithium-ion (Li-ion) battery cell and a battery pack demonstrate that the proposed spatio-temporal inference system can detect and locate the internal short circuit fault before it develops into a thermal runaway.
TL;DR: In this paper , a modeling algorithm with a residual network and a bidirectional novel gated cycle unit is used to predict the clinker exit temperature of the rotary kiln and a dynamic time-delay calculation algorithm based on Mutual Information and adaptive sliding windows is proposed for time series data reconstruction, and finally a Gaussian-weighted multi-model fusion is applied to the prediction results for cement rotary Kilns that produce a wide range of working conditions.
••01 Apr 2012
TL;DR: ELM provides a unified learning platform with a widespread type of feature mappings and can be applied in regression and multiclass classification applications directly and in theory, ELM can approximate any target continuous function and classify any disjoint regions.
Abstract: Due to the simplicity of their implementations, least square support vector machine (LS-SVM) and proximal support vector machine (PSVM) have been widely used in binary classification applications. The conventional LS-SVM and PSVM cannot be used in regression and multiclass classification applications directly, although variants of LS-SVM and PSVM have been proposed to handle such cases. This paper shows that both LS-SVM and PSVM can be simplified further and a unified learning framework of LS-SVM, PSVM, and other regularization algorithms referred to extreme learning machine (ELM) can be built. ELM works for the “generalized” single-hidden-layer feedforward networks (SLFNs), but the hidden layer (or called feature mapping) in ELM need not be tuned. Such SLFNs include but are not limited to SVM, polynomial network, and the conventional feedforward neural networks. This paper shows the following: 1) ELM provides a unified learning platform with a widespread type of feature mappings and can be applied in regression and multiclass classification applications directly; 2) from the optimization method point of view, ELM has milder optimization constraints compared to LS-SVM and PSVM; 3) in theory, compared to ELM, LS-SVM and PSVM achieve suboptimal solutions and require higher computational complexity; and 4) in theory, ELM can approximate any target continuous function and classify any disjoint regions. As verified by the simulation results, ELM tends to have better scalability and achieve similar (for regression and binary class cases) or much better (for multiclass cases) generalization performance at much faster learning speed (up to thousands times) than traditional SVM and LS-SVM.
TL;DR: A survey on Extreme learning machine (ELM) and its variants, especially on (1) batch learning mode of ELM, (2) fully complex ELm, (3) online sequential ELM; and (4) incremental ELM and (5) ensemble ofELM.
Abstract: Computational intelligence techniques have been used in wide applications. Out of numerous computational intelligence techniques, neural networks and support vector machines (SVMs) have been playing the dominant roles. However, it is known that both neural networks and SVMs face some challenging issues such as: (1) slow learning speed, (2) trivial human intervene, and/or (3) poor computational scalability. Extreme learning machine (ELM) as emergent technology which overcomes some challenges faced by other techniques has recently attracted the attention from more and more researchers. ELM works for generalized single-hidden layer feedforward networks (SLFNs). The essence of ELM is that the hidden layer of SLFNs need not be tuned. Compared with those traditional computational intelligence techniques, ELM provides better generalization performance at a much faster learning speed and with least human intervene. This paper gives a survey on ELM and its variants, especially on (1) batch learning mode of ELM, (2) fully complex ELM, (3) online sequential ELM, (4) incremental ELM, and (5) ensemble of ELM.
TL;DR: A Matlab/Octave toolbox for the application of GSA, called SAFE (Sensitivity Analysis For Everybody), which implements several established GSA methods and allows for easily integrating others and embeds good practice guidelines through workflow scripts.
Abstract: Global Sensitivity Analysis (GSA) is increasingly used in the development and assessment of environmental models. Here we present a Matlab/Octave toolbox for the application of GSA, called SAFE (Sensitivity Analysis For Everybody). It implements several established GSA methods and allows for easily integrating others. All methods implemented in SAFE support the assessment of the robustness and convergence of sensitivity indices. Furthermore, SAFE includes numerous visualisation tools for the effective investigation and communication of GSA results. The toolbox is designed to make GSA accessible to non-specialist users, and to provide a fully commented code for more experienced users to complement their own tools. The documentation includes a set of workflow scripts with practical guidelines on how to apply GSA and how to use the toolbox. SAFE is open source and freely available for academic and non-commercial purpose. Ultimately, SAFE aims at contributing towards improving the diffusion and quality of GSA practice in the environmental modelling community. SAFE implements several GSA methods and can easily integrate new ones.SAFE facilitates assessment of robustness/convergence and effective visualization.SAFE embeds good practice guidelines through workflow scripts.SAFE is intended for both non-specialists users and SA developers.
TL;DR: A supervised LSTM (SLSTM) network is proposed to learn quality-relevant hidden dynamics for soft sensor application, which is composed of basic SLSTM unit at each sampling instant.
Abstract: Soft sensor has been extensively utilized in industrial processes for prediction of key quality variables. To build an accurate virtual sensor model, it is very significant to model the dynamic and nonlinear behaviors of process sequential data properly. Recently, a long short-term memory (LSTM) network has shown great modeling ability on various time series, in which basic LSTM units can handle data nonlinearities and dynamics with a dynamic latent variable structure. However, the hidden variables in the basic LSTM unit mainly focus on describing the dynamics of input variables, which lack representation for the quality data. In this paper, a supervised LSTM (SLSTM) network is proposed to learn quality-relevant hidden dynamics for soft sensor application, which is composed of basic SLSTM unit at each sampling instant. In the basic SLSTM unit, the quality and input variables are simultaneously utilized to learn the dynamic hidden states, which are more relevant and useful for quality prediction. The effectiveness of the proposed SLSTM network is demonstrated on a penicillin fermentation process and an industrial debutanizer column.
TL;DR: At the basis of composite energy function, the boundedness and the learning convergence are proved for the closed-loop MAV system, which is composed of a rigid body and two flexible wings under spatiotemporally varying disturbances.
Abstract: This paper addresses a flexible micro aerial vehicle (MAV) under spatiotemporally varying disturbances, which is composed of a rigid body and two flexible wings. Based on Hamilton’s principle, a distributed parameter system coupling in bending and twisting, is modeled. Two iterative learning control (ILC) schemes are designed to suppress the vibrations in bending and twisting, reject the distributed disturbances and regulate the displacement of the rigid body to track a prescribed constant trajectory. At the basis of composite energy function, the boundedness and the learning convergence are proved for the closed-loop MAV system. Simulation results are provided to illustrate the effectiveness of the proposed ILC laws.