Objective: Daytime short nap involves individual physiological states including alertness and drowsiness. In order to have a better understanding of the periodical rhymes then promote good interpretability alertness, aim this study is detect drowsiness during daytime nap. Methods: A method Bayesian-copula discriminant classifier (BCDC) was introduced based on features extracted from electroencephalogram (EEG) signals. As an extension traditional Bayesian decision theory, BCDC tries construct...

This paper presents a simple and theoretically sound submodule-based model to simulate the characteristics of photovoltaic (PV) array with series-parallel configuration. The proposed can describe behavior bypass diodes as well full PV under varying irradiance temperature conditions. Rather than using nonlinear system equations solved Jacobian matrix, separate are employed by an easy-to-implement bisection search method. Consequently, output current be readily determined when its voltage,...

Abstract The refining industry's substantial hydrogen demand relies on high‐carbon‐emission production methods, facing dual challenges of reducing costs and achieving net‐zero emissions. This study proposes a renewable energy‐powered water electrolysis system integrated with seasonal storage to address these challenges. A multi‐objective capacity optimization model is developed minimize carbon emissions, ensuring reliable supply. To manage computational complexity, time variational...

This work presents a simple parameter estimation approach for photovoltaic (PV) module using single-diode five-parameter electrical model. The proposed only uses the information from manufacturer datasheet without requiring specific experimental procedure or curve extractor. number of parameters to be determined is first reduced five two by gaining insight into equations model at standard test conditions (STCs). A nonlinear least square (NLS) objective function then constructed and minimized...

Abstract Model reduction of a high‐dimensional distributed parameter system (DPS) reduces the complexity for various applications, from monitoring to model predictive control, while retaining its intrinsic properties. Unfortunately, assumption time–space separability usually fails hold popular separation methods because space and time DPS are inherently coupled. In this study, coupled learning method data‐driven is presented. The proposed has advantage preserving coupling characteristics...

Uncertainty is ubiquitous throughout engineering design processes. Robust optimization (RO) aims to find optimal solutions that are relatively insensitive input uncertainty. In this paper, a new approach presented for single-objective RO problems with an objective function and constraints continuous differentiable. Both the variables parameters interval uncertainties represented as affine forms. A mixed arithmetic (IA)/affine (AA) model subsequently utilized in order obtain approximations...

Abstract Nonlinear high‐dimensional distributed parameter systems (DPSs) described by sets of parabolic partial different equations (PDEs) exhibit a dominant, low‐dimensional slow behavior that can be captured using model reduction. A time–space‐coupled reduction architecture combining encoder–decoder networks with recurrent neural (RNNs) was presented in our previous work, for modeling the spatiotemporal dynamics DPSs without recourse to governing equations. In this we further understand...

This paper presents a label propagation controlled islanding (LPCI) algorithm for large interconnected power systems. The proposed is suitable finding an solution quickly when system with disturbances need to avoid uncontrolled separation and prevent wide‐area blackouts. By referring the graph theory system, similar first introduced according branch flows system. Then, buses of coherent generators are viewed as labeled data, while left unlabeled data. As result, problem converted into...

As a data-driven, equation-free decomposition method, the DMD can characterise dynamic behaviour of non-linear system by using modes and eigenvalues. However, all current provable algorithms suffer from separate procedure for obtaining determining number modes. In this study, authors propose nuclear norm regularised (NNR-DMD) algorithm that produces low-dimensional spatio-temporal A regularisation term is added to optimisation problem standard prompting sparsity projected Split Bregman...

Fluctuations of state variables play a pivotal role in analyzing small signal stability the power system due to integration renewable energy sources. This paper develops theoretical analysis methodology by using spectral density (PSD) for capturing frequency and amplitude variable fluctuations heterogeneous systems with stochastic excitations. The generation consumption occurring simultaneously are modeled Ornstein–Uhlenbeck processes. PSDs can be analytically calculated. PSD-based...

We propose a face recognition method named the Laplacian+OPRA-faces approach based on Laplacian-faces and OPRA-faces approach. An explicit mapping from high-dimensional data space to reduced is obtained. The aims preserve both local structure of Local Preserving Projections (LLE) geometry Orthogonal Projection Reduction by Affinity (OPRA). Several improved measures selection parameters in supervised case are presented. compare proposed with eigenfaces, fisherfaces, Laplacianfaces two...

This study provides a new algorithm for grouping coherent generators in power systems using robust principal component analysis. In coherency identification of based on measurements by phasor measurement unit (PMU), PMU can become unavailable because unexpected failure data acquisition or communication links. However, the proposed is to missing and demonstrated an IEEE 16‐generator 68‐bus system. effective clusters with validated compared results obtained analysis independent methods. © 2015...

The robust compressed sensing problem subject to a bounded and structured perturbation in the matrix is solved two steps. alternating direction method of multipliers (ADMM) first applied obtain support set. Unlike existing signal recovery solutions, proposed optimisation convex. ADMM algorithm that every subproblem has global minimum employed solve problem. Then, standard regularised least-squares restrained reduce error. numerical tests show approach provides estimation set, although it...

The paper develops a generalized-extended-state-observer-based sliding mode control (GESO-SMC) approach for temperature stabilization of continuous stirred tank reactor (CSTR) with mismatched disturbances. generalized extended state observer (GESO) is proposed to generate the estimates both unmeasured states and Based on estimated variables, parameters function are designed controller counteract effect disturbances CSTR. Furthermore, optimized switching gain found via using genetic algorithm...

With the continuous development and progress of photovoltaic (PV) technology, proportion PV generation in microgrids has increased significantly, making more low-carbon environmentally friendly. However, high penetration can lead to excessive fluctuations exchange power at point common coupling (PCC), resulting transmission line overheating posing risks microgrid's stability. Aiming address active PCC caused by generation, paper proposes a new cooperative dispatch algorithm based on...

According to the non-probabilistic finite element algorithms, random equations are translated into interval equations. Firstly, with concept of confidence in probability, a number can be taken as variable uniform distribution. Secondly, Monte Carlo (MC) method and optimization presented. Finally, example shown, when numbers parameters small, two algorithms all effective. But large, only has stabilized solving ability.

The performance of popular and classical k-nearest neighbor classifier depends on the distance metric. Large margin nearest using gradient optimization method is prone to local minima. In this paper, we present a Mahalanobis metric learning based cutting plane algorithm which reduces largely constraints for solving semidefinite programming problem. Experimental results ITC I data sets show that our can achieve promising speedups compared with under similar training, test error rates.