Search Results for « Phase Identification »

Based on expanded memory polynomial (EMP) and orthogonal EMP (OEMP), this paper addresses the nonlinear system identification (NSI) problem by introducing an amplitudephase-frequency (APF) characterization framework to enable performance measurability, monotonicity, and optimality. APF characteristic functions (CFs) are defined to better capture the nonlinear distortions arising from both the amplitude and phase of the input signal. It is shown that an APF CF can be expressed as a weighted sum of characteristic basis functions (CBFs) or their orthogonal counterparts (OCBFs), with the associated CBF or OCBF frequency responses directly linked to the EMP or OEMP coefficients. As a result, NSI can be carried out by alternatively estimating the CBF or OCBF frequency responses. The paper also presents detailed estimation methods employing frequency modulated continuous wave (FMCW) test signals and analyzes different mean-squared-error (MSE) measurement strategies. Simulation and experimental results using a 73.5 GHz carrier frequency and 2.125 GHz bandwidth millimeter wave communication system validate the effectiveness and practical applicability of the proposed technique.

The continued development of energy materials such as Lithium-ion battery materials and superconductors is important for the overall improvement of energy storage and transportation methods. Raman spectroscopy, commonly used for phase identification, is effective but may lead to misleading results for some materials due to the (relatively) high laser energies used. Difficulties encountered during Raman experimentation involving carbon-coated battery materials sensitive to laser heating (LFP, LTO) are discussed alongside potential solutions. Raman spectroscopy is also used alongside THz spectroscopy and DFT calculations to examine the superconductor Nb3Sn, results are discussed that challenge existing assumptions this material's structure.

Abstract Artifact elimination has become an inseparable part while processing electroencephalogram (EEG) in most brain computer interface (BCI) applications. Scientists have tried to introduce effective and efficient methods which can remove artifacts and also reserve desire information pertaining to brain activity. Blind source separation (BSS) methods have been receiving a great deal of attention in recent decades since they are considered routine and standard signal processing tools and are commonly used to eliminate artifacts and noise. Most studies, mainly EEG-related ones, apply BSS methods in preprocessing sections to achieve better results. On the other hand, BSS methods should be followed by a classifier in order to identify artifactual sources and remove them in next steps. Therefore, artifact identification is always a challenging problem while employing BSS methods. Additionally, removing all detected artifactual components leads to loss of information since some desire information related to neural activity leaks to these sources. So, an approach should be employed to suppress the artifacts and reserve neural activity. In this study, a new hybrid method is proposed to automatically separate and identify electroencephalogram (EEG) sources with the aim of classifying and removing artifacts. Automated source identification is still a challenge. Researchers have always made efforts to propose precise, fast and automated source verification methods. Reliable source identification has always been of great importance. This paper addresses blind source separation based on second order blind identification (SOBI) as it is reportedly one of the most effective methods in EEG source separation problems. Then a new method for source verification is introduced which takes advantage of components phase spaces and their dynamics. A new state space called angle space (AS) is introduced and features are extracted based on the angle plot (AP) and Poincare planes. Identified artifactual sources are eliminated using stationary wavelet transform (SWT). Simulated, semi-simulated and real EEG signals are employed to evaluate the proposed method. Different simulations are performed and performance indices are reported. Results show that the proposed method outperforms most recent studies in this subject.

Dual-phase (DP) steels exhibit complex macroscopic mechanical behaviour that arises from the interaction between the ferrite and martensite constituents. Accurate phase-specific constitutive behaviour is therefore important for micromechanical finite element analysis; however, direct experimental determination of individual phase flow curves remains challenging. This study evaluates the Micromechanical Adaptive Iteration Algorithm (MAIA) as a numerical inverse-modelling framework for determining ferrite and martensite flow curves from the macroscopic tensile response of various DP steels. A three-dimensional micromechanical finite element model was developed using artificially generated discrete microstructure to represent the ferrite–martensite phase distribution in the DP steels. The MAIA iteratively updated the phase-specific flow curves by minimizing discrepancies between predicted and experimental macroscopic stress–strain responses. Four groups of DP steels with different chemical composition and intercritical annealing conditions were investigated, yielding nine sets of phase-specific flow curves. The calculated phase responses demonstrate that MAIA effectively captured key metallurgical trends, including the carbon-dependent strengthening of martensite and the influence of intercritical annealing temperature on martensite strength, whereas ferrite exhibited comparatively limited changes. MAIA achieved convergence within approximately 2–3 iterations for the investigated materials, indicating the computational efficiency of the approach in reducing repeated finite element simulations. These findings confirm the applicability and potential of MAIA as an efficient numerical inverse-modelling framework for extracting phase-specific constitutive behaviour in DP steels while reducing the dependance on extensive direct experimental characterization.

Phase identification is the problem of determining what phase(s) that a load is connected to in a power distribution system. However, real-world sensor measurements used for phase identification have some level of noise that can hamper the ability to identify phase connections using data-driven methods. Knowing the phase connections is important to keep the distribution system balanced so that parts of the system are not overloaded, which can lead to inefficient operations, accelerated component degradation, and system destruction at worst. We use Singular Value Decomposition (SVD) with the optimal Singular Value Hard Threshold (SVHT) as part of a feature engineering pipeline to denoise data matrices of voltage magnitude measurements. This approach reduces Frobenius error and increases the average phase identification accuracy over a year of time series data. K- medoids clustering is used on the denoised voltage magnitude measurements to perform phase identification.