Search Results (97)

This study introduces and investigates the space of interval-valued Abel-convergent sequences, extending the classical notion of Abel convergence to sequences whose terms are closed and bounded intervals rather than real numbers. A suitable metric structure is defined on this space, and it is shown that the space is complete with respect to the introduced metric. Additionally, it is proven that the space of interval-valued Abel-convergent sequences forms a quasilinear subspace. Moreover, several inclusion relations are examined, and a norm is defined under which the space becomes a normed quasilinear space. Full article

At temperatures higher than 5 °C in the cooling chambers of refrigeration systems, bacteria multiply rapidly on fresh fishes, thereby leading to an increased risk of foodborne diseases. Maintaining the storage temperature within the recommended bounds of 0 °C and 5 °C is needed to maintain food safety and quality. This study presents model predictive control of a data-driven medium-temperature cold storage system using subspace system identification techniques. The identified linear model presents a holistic view of the whole system, with each subsystem cohesively linked together. The data-driven model was developed from synthetic data derived from a high-fidelity simulation benchmark model of a supermarket refrigeration system from Aalborg University, Denmark. The benchmark model consists of a medium-temperature closed display case, the suction manifold, and the compressor rack. The data of the expansion valve, suction pressure, compressor capacity, heat transfer rate, and ambient temperature were taken as inputs, while the data of the air and goods temperatures were taken as outputs based on expert knowledge. A linear model predictive controller was designed to control the temperature outputs of the identified linear model, and the outputs were compared with the proportional–integral dead band control used in the benchmark. Simulation results for 24 h showed that the model predictive controller was able to achieve an air temperature and a goods temperature within the recommended temperature range of 0 °C and 5 °C that guarantees safe storage of fresh fishes. These results imply that a reduced-order model of a commercial refrigeration system that is robust, reliable, and stable can be developed and controlled to achieve the goal of food safety, thereby guaranteeing food security and reducing costs. Full article

Subspace algorithms like canonical variate analysis (CVA) are regression-based methods for the estimation of linear dynamic state space models. They have been shown to deliver accurate (consistent and asymptotically equivalent to quasi-maximum likelihood estimation using the Gaussian likelihood) estimators for stably invertible stationary autoregressive moving average (ARMA) processes. These results use the assumption that there are no zeros of the spectral density on the unit circle corresponding to the state space system. In this technical study, we consider vector processes made stationary by applying differencing to all variables, ignoring potential co-integrating relations. This leads to spectral zeros violating the above mentioned assumptions. We show consistency for the CVA estimators, closing a gap in the literature. However, a simulation exercise shows that over-differencing (while leading to consistent estimation of the transfer function) also complicates inference for CVA estimators, not just maximum likelihood-based estimators. This is also demonstrated in a real-world data example. The result also applies to seasonal differencing. The present paper hence suggests working with original data, not working in differences. Full article

It is known that the continuous-time variant of Grover’s search algorithm is characterized by quantum search frameworks that are governed by stationary Hamiltonians, which result in search trajectories confined to the two-dimensional subspace of the complete Hilbert space formed by the source and target states. Specifically, the search approach is ineffective when the source and target states are orthogonal. In this paper, we employ normalization, orthogonality, and energy limitations to demonstrate that it is unfeasible to breach time-optimality between orthogonal states with constant Hamiltonians when the evolution is limited to the two-dimensional space spanned by the initial and final states. Deviations from time-optimality for unitary evolutions between orthogonal states can only occur with time-dependent Hamiltonian evolutions or, alternatively, with constant Hamiltonian evolutions in higher-dimensional subspaces of the entire Hilbert space. Ultimately, we employ our quantitative analysis to provide meaningful insights regarding the relationship between time-optimal evolutions and analog quantum search methods. We determine that the challenge of transitioning between orthogonal states with a constant Hamiltonian in a sub-optimal time is closely linked to the shortcomings of analog quantum search when the source and target states are orthogonal and not interconnected by the search Hamiltonian. In both scenarios, the fundamental cause of the failure lies in the existence of an inherent symmetry within the system. Full article

Modern robotic tasks often require interaction with the surrounding elements in the workspace. In some high-precision tasks, it is essential to stabilize the contact force on a smooth yet rigid surface, which can be modeled as a unilateral constraint. This challenge becomes increasingly complex in the presence of disturbances. This study addresses these issues using a robust fuzzy force-position controller that combines the approximation capabilities of fuzzy inference systems with the nonlocal properties of fractional operators. The proposed approach extends the error integration to include proportional-integral-derivative (PID) components of the position error, along with the integral of the contact force error. This formulation leverages the orthogonality between force and velocity subspaces to achieve accurate force-position stabilization. Additionally, an adaptive mechanism enhances closed-loop performance and robustness. The effectiveness of the proposed controller is validated through analytical derivations and simulations, thereby demonstrating its reliability in constrained environments. Full article

Accurate displacement field measurement by holographic interferometry requires robust analysis of high-density fringe patterns, which is hindered by speckle noise inherent in any interferogram, no matter how perfect. Conventional skeletonization methods, such as edge detection algorithms and active contour models, often fail under these conditions, producing fragmented and unreliable fringe contours. This paper presents a novel skeletonization procedure that simultaneously addresses three fundamental challenges: (1) topology preservation—by representing the fringe family within a physics-informed, finite-dimensional parametric subspace (e.g., Fourier-based contours), ensuring global smoothness, connectivity, and correct nesting of each fringe; (2) extreme noise robustness—through a robust strip integration functional that replaces noisy point sampling with Gaussian-weighted intensity averaging across a narrow strip, effectively suppressing speckle while yielding a smooth objective function suitable for gradient-based optimization; and (3) sub-pixel accuracy without phase extraction—leveraging continuous bicubic interpolation within a recursive quasi-optimization framework that exploits fringe similarity for precise and stable contour localization. The method’s performance is quantitatively validated on synthetic interferograms with controlled noise, demonstrating significantly lower error compared to baseline techniques. Practical utility is confirmed by successful processing of a real interferogram of a bent plate containing over 100 fringes, enabling precise displacement field reconstruction that closely matches independent theoretical modeling. The proposed procedure provides a reliable tool for processing challenging interferograms where traditional methods fail to deliver satisfactory results. Full article

This paper relies on orthogonal metric spaces related to cyclic self-mappings and some of their relevant properties. The involved binary relation is not symmetric, and then the term pseudo-orthogonality will be used for the relation used in the article to address the established results on cyclic self-mappings. Firstly, some orthogonal binary relations are given through examples to fix some ideas to be followed in the main body of the article. It is seen that the orthogonal elements of the orthogonal sets are not necessarily singletons. Secondly, “ad hoc” specific orthogonality binary relations are also described through examples related to the investigation of stability and controllability problems in dynamic systems. The main objective of this paper is to investigate the properties of cyclic single-valued self-mappings on the union of any finite number $p\ge 2$ of nonempty closed subsets of a metric space in a cyclic disposal under an “ad hoc” defined pseudo-orthogonality condition. Such a condition is defined on certain subsequences, referred to as pseudo-orthogonal sequences, rather than on the whole generated sequences under the self-mapping. It basically consists of a cyclic, in general iteration-dependent, contractive condition just for such subsequences which, on the other hand, are not forced as a constraint to be fulfilled by the whole sequences. Furthermore, the whole sequences in which those sequences are contained are allowed to be locally non-contractive or even locally expansive. The boundedness and the convergence properties of distances between pseudo-orthogonal subsequences and sequences are investigated under the condition that one of the subsets has a unique best proximity point to its adjacent subset in the cyclic disposal to which the pseudo-orthogonal subsequences converge. The pseudo-orthogonal metric subspace of the given metric space is proved to be complete although the whole metric space is not assumed to be complete. The pseudo-orthogonal element is seen to be a set of best proximity points, one per subset of the cyclic disposal, although it is not required for all the best proximity sets to be singletons. It is proved that the pseudo-orthogonal subsequences converge to a limit cycle, consisting of a best proximity point per subset of the cyclic disposal, which is also the pseudo-orthogonal element. The whole sequences are also proved to be bounded and the distances between their elements in adjacent subsets are also proved to converge to the distance between adjacent subsets. In the event that the metric space is a uniformly convex Banach space, it suffices that one of the subsets of the cyclic disposal be boundedly compact with its best proximity set being a singleton. In this case, the pseudo-orthogonal sequences converge to their best proximity set to their adjacent subset provided that such a best proximity set is a singleton. Full article

Current multimodal imaging–based source localization (SoL) methods often rely on synchronously recorded data, and many neural network–driven approaches require large training datasets, conditions rarely met in clinical neuroimaging. To address these limitations, we introduce MieSoL (Multimodal Mutual Information Extraction and Source Localization), a unified framework that fuses EEG and MRI, whether acquired synchronously or asynchronously, to achieve robust cross-modal information extraction and high-accuracy SoL. Targeting neuroimaging applications, MieSoL combines Magnetic Resonance Imaging (MRI) and Electroencephalography (EEG), leveraging their complementary strengths—MRI’s high spatial resolution and EEG’s superior temporal resolution. MieSoL addresses key limitations of existing SoL methods, including poor localization accuracy and an unreliable estimation of the true source number. The framework combines two existing components—Unified Left Eigenvectors (ULeV) and Efficient High-Resolution sLORETA (EHR-sLORETA)—but integrates them in a novel way: ULeV is adapted to extract a noise-resistant shared latent representation across modalities, enabling cross-modal denoising and an improved estimation of the true source number (TSN), while EHR-sLORETA subsequently performs anatomically constrained high-resolution inverse mapping on the purified subspace. While EHR-sLORETA already demonstrates superior localization precision relative to sLORETA, replacing conventional PCA/ICA preprocessing with ULeV provides substantial advantages, particularly when data are scarce or asynchronously recorded. Unlike PCA/ICA approaches, which perform denoising and source selection separately and are limited in capturing shared information, ULeV jointly processes EEG and MRI to perform denoising, dimension reduction, and mutual-information-based feature extraction in a unified step. This coupling directly addresses longstanding challenges in multimodal SoL, including inconsistent noise levels, temporal misalignment, and the inefficiency of traditional PCA-based preprocessing. Consequently, on synthetic datasets, MieSoL achieves 40% improvement in Average Correlation Coefficient (ACC) and 56% reduction in Average Error Estimation (AEE) compared to conventional techniques. Clinical validation involving 26 epilepsy patients further demonstrates the method’s robustness, with automated results aligning closely with expert epileptologist assessments. Overall, MieSoL offers a principled and interpretable multimodal fusion paradigm that enhances the fidelity of EEG source localization, holding significant promise for both clinical and cognitive neuroscience applications. Full article

We construct the free group over a non-Archimedean fuzzy metric space $(X,M,\wedge )$ in the sense of George and Veeramani where ∧ is the minimum t-norm. The two main tools used are the concept of a scheme (for every non-empty subset S of $\mathbb{N}$ of even cardinal, a permutation $\phi$ on S is a scheme for S if it is idempotent, with no fixed points and, additionally, $i<j<\phi \left(i\right)<\phi \left(j\right)$ does not hold for every $i,j\in S$), and the notion of a fuzzy prenorm on a fuzzy topological group. As a consequence of our results, we prove that every non-Archimedean fuzzy metric space $(X,M,\wedge )$ in the sense of George and Veeramani is isometric to a closed subspace of a non-Archimedean fuzzy metric free (Abelian) group and also that every metric space $(X,d)$ is uniformly isomorphic to a closed subspace of a non-Archimedean fuzzy metric free (Abelian) group. Our results also apply to non-Archimedean fuzzy metric spaces in the sense of Kramosil and Michálek. Full article

A vibration-based protocol is presented for identifying barely visible impact damage (BVID) in type-IV composite-overwrapped pressure vessels (COPVs). A 1 kJ hemispherical-tip strike was applied to a fully pressurized vessel, which was subsequently depressurized and characterized by free–free experimental modal analysis over a 168-point grid. The frequency response functions (FRFs) at the impact meridian exhibited distinct peaks near 3.70, 4.34, and 4.90 kHz with larger amplitudes and lower coherence than at the diametrically opposite meridian, indicating local circumferential stiffness loss. A detailed finite element model of the liner, bosses, and carbon/epoxy overwrap was updated by idealizing a cylindrical sub-volume with a 90% reduction in orthotropic stiffness. The pristine and “damaged” numerical modal sets agreed closely (mean frequency error < 2%), and for most of the first 60 modes, the diagonal Modal Assurance Criterion (MAC) remained ≥ 0.90. However, in several nearly degenerate circumferential mode pairs, the diagonal MAC dropped to 0.49–0.88 because the local asymmetry rotated the eigenvectors within a common subspace, showing that classical MAC alone cannot expose such early-stage defects. Radial displacement scan-lines provided the missing spatial resolution. Modes whose antinodal regions intersect the dent showed pronounced local amplitude bulges and slight angular shifts in the peak toward the impact site, whereas modes with a nodal line across the damage were virtually unchanged. The combined use of FRF asymmetry, MAC screening, and scan-line deformation profiling localized the impact to the correct circumferential sector with centimeter-scale resolution along the scan ring, yielding predictive signatures for rapid, non-pressurized in situ assessment of impacted COPVs after depressurization. Full article

Traditional time-frequency diagnostics for high-speed bearings face an entrenched trade-off between resolution and real-time feasibility. We present a fast Dual-Phase Short-Time Root-MUSIC pipeline that exploits Hankel structure via FFT-accelerated Lanczos bidiagonalization and Sliding-window Singular Value Decomposition to deliver sub-Hz super-resolution under millisecond budgets. Validated on the Politecnico di Torino aerospace dataset (seven fault classes, three severities), fDSTrM detects 150 μm inner-race and rolling-element defects with 98% and 95% probability, respectively, at signal-to-noise ratio down to −3 dB (78% detection), while Short-Time Fourier Transform and Wavelet Packet Decomposition fail under identical settings. Against classical Root-MUSIC, the approach sustains approximately 200 times speedup with less than ${10}^{-11}$ relative frequency error in offline scaling, and achieves 1.85 milliseconds per 4096-sample frame on embedded-class hardware in streaming tests. Subspace order pre-estimation with adaptive correction preserves closely spaced components; Kalman tracking formalizes uncertainty and yields 95% confidence bands. The resulting early warning margin extends maintenance lead-time by 24–72 h under industrial interferences (Gaussian, impulsive, and Variable Frequency Drive harmonics), enabling field-deployable super-resolution previously constrained to offline analysis. Full article

A multivector direct model predictive control (DMPC) scheme is proposed for the dual three-phase permanent magnet synchronous machine (DTP-PMSM) drive system to achieve closed-loop control for both fundamental current tracking and harmonic current minimization. The proposed multivector DMPC scheme employs four active voltage vectors, including two large vectors and two basic vectors for implicit modulation. Moreover, the control optimization problem is formulated as a four-dimensional quadratic programming problem, which is suitable for real-time implementation. The proposed multivector DMPC scheme enables fast and accurate tracking of the fundamental current as well as effective suppression of harmonic currents in both the fundamental and harmonic subspaces. In addition, a Kalman filter observer is incorporated to enhance robustness against model uncertainties and disturbances. Experimental results on a DTP-PMSM test bench verify that the proposed multivector DMPC scheme effectively reduces torque ripple, improves current quality, and enhances both steady-state and transient performance of the system. Full article

To address the computational efficiency challenges in two-dimensional (2D) direction-of-arrival (DOA) estimation, a two-stage framework integrating the Nyström approximation with subspace decomposition techniques is proposed in this paper. The methodology strategically integrates the Nyström approximation with subspace decomposition techniques to bridge the critical performance–complexity trade-off inherent in high-resolution parameter estimation scenarios. In the first stage, the Nyström method is applied to approximate the signal subspace while simultaneously enabling construction of a reduced rank covariance matrix, which effectively reduces the computational complexity compared with eigenvalue decomposition (EVD) or singular value decomposition (SVD). This innovative approach efficiently derives two distinct signal subspaces that closely approximate those obtained from the full-dimensional covariance matrix but at substantially reduced computational cost. The second stage employs a sophisticated subspace-based estimation technique that leverages the principal singular vectors associated with these approximated subspaces. This process incorporates an iterative refinement mechanism to accurately resolve the paired azimuth and elevation angles comprising the 2D DOA solution. With the use of the Nyström approximation and reduced rank framework, the entire DOA estimation process completely circumvents traditional EVD/SVD operations. This elimination constitutes the core mechanism enabling substantial computational savings without compromising estimation accuracy. Comprehensive numerical simulations rigorously demonstrate that the proposed framework maintains performance competitive with conventional high-complexity estimators while achieving significant complexity reduction. The evaluation benchmarks the method against multiple state-of-the-art DOA estimation techniques across diverse operational scenarios, confirming both its efficacy and robustness under varying signal conditions. Full article

Multiphase electric drives (EDs) offer important advantages for high-demand applications. However, they require appropriate high-performance control strategies. In this context, finite-control-set model predictive control (FCS-MPC) emerges as a promising strategy, offering a notable flexibility to implement multiobjective regulation schemes. When applied to multiphase EDs, standard FCS-MPC exhibits degraded current quality at low and medium control frequencies. Multivector solutions address this issue by properly combining multiple voltage vectors within a single control period to create the so-called virtual voltage vectors (VVVs). In this way, this approach achieves flux and torque regulation while minimizing current injection into the secondary subspace. For this purpose, the VVV synthesis typically prioritizes active vectors with low contribution in secondary subspaces, avoiding the average deception phenomenon. VVV solutions commonly enable an open-loop regulation of secondary currents. Nevertheless, the absence of closed-loop control in the secondary subspace hinders the compensation of nonlinearities, machine asymmetries, and unbalanced conditions in the ED. Considering this scenario, this work implements a multivector FCS-MPC recovering closed-loop control for the secondary subspace. The capability of the proposal to mitigate secondary current injection and compensate for possible dissymmetries is experimentally evaluated in a six-phase ED. Its performance is compared against a benchmark technique in which secondary current regulation is handled in open-loop mode. The proposed control solution significantly improves in current quality, achieving a reduction in harmonic distortion of 54% at medium speed. Full article

Objective: This study aimed to explore the acute neurophysiological effects of a single oral dose of Astragaloside IV (AS-IV) on EEG-measured brain oscillations and cognitive-relevant spectral markers in healthy young adults. Methods: Twenty healthy adults (8 females, 12 males; mean age: $23.4\pm 2.1$) underwent eyes-closed resting-state EEG recordings before and approximately 90 min after oral intake of 150 mg AS-IV. EEG data were collected using a 21-channel 10–20 system and cleaned via Artifact Subspace Reconstruction and Independent Component Analysis. Data quality was confirmed using a signal-to-noise ratio and 1/f spectral slope. Absolute and relative power values, band ratios, and frontal alpha asymmetry were computed. Statistical comparisons were made using paired t-tests or Wilcoxon signed-rank tests. Results: Absolute power decreased in delta, theta, beta, and gamma bands (p < 0.05) but remained stable for alpha. Relative alpha power increased significantly (p = 0.002), with rises in relative beta, theta, and delta and a drop in relative gamma (p = 0.003). Alpha/beta and theta/beta ratios increased, while delta/alpha decreased. Frontal alpha asymmetry was unchanged. Sex differences were examined in all measures that showed significant changes; however, no sex-dependent effects were found. Conclusions: A single AS-IV dose may acutely modulate brain oscillations, supporting its potential neuroactive properties. Larger placebo-controlled trials, including concurrent psychometric assessments, are needed to verify and contextualize these findings. A single AS-IV dose may acutely modulate brain oscillations, supporting its potential neuroactive properties. Full article