Direct Method Research Articles

Equivalence of Impedance Participation Analysis Methods for Hybrid AC/DC Power Systems

Participation analysis based on whole-system impedance models enables the root-cause stability analysis of large-scale power systems when state-space models are unavailable. To extend participation analysis to the hybrid AC/DC system, especially for the interlinking converter, this paper proposes two complementary methods, namely the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">direct method</i> and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">indirect method</i> . The direct method treats the interlinking converter as hybrid AC-DC port apparatus and the participation factor can be defined with the terminal hybrid AC-DC impedance matrix correspondingly. As for the indirect method, system equivalence can be made at the AC or DC terminal of the interlinking converter when impedance models within subsystems are unavailable such that preliminary knowledge of systems can be simplified. Importantly, the equivalence of these two methods on the observability of system components is revealed through theoretical analysis. The proposed impedance participation analysis methods are illustrated with a 4-bus hybrid AC/DC system, a modified 28-bus hybrid AC/DC system and a modified 96-bus hybrid AC/DC system. Numerical calculations and time-domain simulations are performed to validate the theoretical analysis.

First Report of Hibiscus Chlorotic Ringspot Virus infecting Hibiscus rosa-sinensis in Italy.

Hibiscus chlorotic ringspot virus (HCRSV) belongs to the Betacarmovirus genus of the Tombusviridae family and is a positive-sense monopartite single-stranded RNA virus. HCRSV was first described in a hibiscus variety imported into the United States from El Salvador. The HCRSV natural host range is limited to species belonging to Malvaceae family, in particular Hibiscus rosa-sinensis, H. manihot, H. diversifolius and H. syriacus. In September 2023, leaf mottle and chlorotic spots were observed on the leaves of two H. rosa-sinensis plants located in a public garden of Ercolano (Naples city, Southern Italy). Two leaves, one from each symptomatic plant, were pooled and submitted to double-stranded RNA extraction using a Viral Gene-Spin Viral DNA/RNA Extraction Kit (iNtRON, Korea), followed by cDNA library preparation with the TruSeq Stranded Total RNA (Illumina, USA). Sequencing on the Illumina NovaSeq 6000 platform (Illumina, USA) with 150-bp paired-end reads yielded 22,578,913 raw reads. 21,413,571 clean reads were obtained by quality control on the sequencing data was performed with the software FastQC (v. 0.11.5). Then low quality bases and adapters were removed with the software BBDuk in the BBTools package setting a minimum read quality of 25 and minimum read length of 35 bp. The resulted filtered reads were used to assembly viral genome by using two different algorithms (Metaspades and RNAViral) implemented in SPAdes (v. 3.15.3). Of the total contigs de novo assembled, the two algorithms implemented identified respectively 340 and 559 contigs related to viruses. BLASTn analysis of the contigs identify one coting of 3965 nt covering 97% to 100% of the whole genome sequence of nine HCRSV isolates, with percentage of identity of 87.8-95.2%. No other plant viruses or viroids have been identified during bioinformatic analysis. To confirm the result, a full-length genomic sequence of the Italian HCRSV isolate (named Ita-1), was obtained by reverse transcription polymerase chain reaction (RT-PCR) using specific primers designed on the sequence of the assembled contigs. The PCR products were sequenced using the Sanger method (Microsynth Seqlab, Germany) in both directions. The obtained full-length genomic sequence of the HCRSV isolate (Acc. No. OR981792) was 3910 nt in length. Maximum-likelihood phylogenetic trees inferred from the whole genome sequence showed that Ita-1 clustered closely with HCRSV isolates. The leaf samples were further analyzed using a HCRSV ELISA kit (Agdia, USA). Healthy H. rosa-sinensis leaves were taken as a negative control and buffer solution as a blank control. The results showed a positive reaction for the two symptomatic plants (OD = 1.345 ± 0.010 at 405 nm) relative to the negative (OD = 0.097) and blank (OD = 0.065) controls. Overall, the results of serological and molecular analyses supported that symptoms observed in H. rosa-sinensis were strictly associated to HCRSV infection. Four viruses have been reported in H. rosa-sinensis in Italy so far: eggplant mottled dwarf nucleorhabdovirus (EMDV), hibiscus latent Fort Pierce virus (HLFPV), hibiscus latent ringspot virus (HLRSV), hibiscus latent Singapore virus (HLSV). To our knowledge, this is the first report of HCRSV in H. rosa-sinensis in Italy, where probably infected plants were accidentally introduced. At present, HCRSV has been reported in three continents (Asia, Oceania and North America) and the number of countries where the virus has been detected is likely to increase rapidly as a result of increased surveillance and availability of diagnostic methods. This study will help the management of viral diseases on H. rosa-sinensis in Italy.

Anisotropic and hierarchical porous boron nitride/graphene aerogels supported phase change materials for efficient solar-thermal energy conversion

The formation of shape-stabilized composite phase change materials (CPCMs) with special structure is an efficient strategy to prevent the leakage of phase change materials (PCMs) and improve the solar-thermal conversion ability. In this work, graphene and boron nitride nanosheets (BBNS) were prepared by the magnesiothermic reduction SHS method in CO2 and N2 atmosphere, respectively. Then, an anisotropic and hierarchical porous boron nitride/graphene aerogel with three-dimensional network was constructed by directional ice-freezing method using carboxymethyl cellulose as supporting framework and adhesive, and polyethylene glycol (PEG) was impregnated to prepare novel CPCMs. The CPCMs had enthalpies of 150∼160 J/g, and a highest thermal conductivity of 0.390 W/(m·K), which was around 47% higher than that of pure PEG. The ability of the CPCMs to convert solar energy to thermal energy was significantly enhanced. Under a simulated light intensity of sunlight (100 mW/cm2), its solar-thermal conversion efficiency reaches 94.92%, and after 20 min of irradiation, the surface temperature can reach 72 °C. These CPCMs are promising for the efficient use of renewable solar energy due to their high solar-thermal conversion efficiency and thermal storage capabilities.

Fostering critical thinking and self-efficacy in mathematics students

This research addresses the low critical mathematical thinking abilities of students when facing mathematical problem-solving challenges. The study aims to evaluate the impact of implementing various teaching approaches, including Contextual Teaching and Learning (CTL), Problem-Based Learning (PBL), and Direct Instruction (DI) models, on students' critical mathematical thinking abilities. Participants included 276 eighth-grade students from SMP La Tansa Mashiro 1, 2, and 3 in Lebak Banten during the academic year 2022/2023, divided into three classes. Samples were selected using simple random sampling, resulting in 94 students for CTL, 92 students for PBL, and 90 students for DI instructional models. Data collection involved self-efficacy questionnaires and critical mathematical thinking ability tests, analyzed using a two-way ANOVA. The findings suggest that implementing CTL and PBL models positively influences students' critical mathematical thinking abilities. Additionally, students' self-efficacy levels contribute significantly to their critical mathematical thinking abilities. Furthermore, a significant interaction was observed between the implementation of CTL, PBL, and DI models and students' efficacy levels, collectively influencing students' critical mathematical thinking abilities. These results emphasize the importance of employing active approaches, such as contextual, problem-based, and direct instructional methods, while also enhancing students' self-confidence to improve their critical mathematical thinking abilities. It is hoped that implementing these recommendations will enhance the effectiveness of mathematics learning and foster the development of students' critical thinking skills.

Piloting a Supplemental Assessment Tool with Younger Residents of Long-Term Care.

Young adults living with disabilities may sometimes end up in long-term care facilities which may not always meet their needs. Our project set out to pilot a supplemental assessment tool, a questionnaire to be used upon admission of younger adults into long-term care. We wanted the opinions of both staff and younger residents on what modifications may be needed in the implementation processes to ensure effectiveness of the tool. This project followed a qualitative design, implementing a previously designed supplemental assessment tool with five staff members and seven younger residents of two long-term care homes in Halifax, Nova Scotia. Residents completed the questionnaire with members of staff involved in admissions. Each group participated in follow-up interviews regarding their thoughts on implementation of the tool. Responses were analyzed using the constructs of the Consolidated Framework in Implementation Research following direct content analysis methods. Feedback from residents and staff suggested that the tool could not be used as a one-size-fits-all solution but that flexibility in the format, content, and structure of the tool would be beneficial to ensure its utility in a variety of settings. Issues raised by staff and residents included, but were not limited to, accessibility of the intervention, the availability of resources, the format of the intervention and topics covered within it, and ensuring that processes for implementation are clearly defined. Both staff and residents approved of the tool for use in the admissions process and agreed that it would enhance the admissions practices already in place.

Numerical simulation of the flow and output of a Savonius hydraulic turbine using the lattice Boltzmann method

The flow and output of a Savonius hydraulic turbine rotor were simulated using the lattice Boltzmann method (LBM). The rotor, characterized by a configuration featuring two semi-circular arc-shaped blades, operated at a Reynolds number of 1.1 × 105. The simulations were conducted in a two-dimensional domain, focusing on the incompressible flow within the cross-sectional area of the rotor perpendicular to its rotational axis. The LBM approach was coupled with a rotor rotation analysis. In the LBM framework, the non-orthogonal central moment model was employed for the precise computation of particle collisions. Additionally, the direct forcing method was used to consider the rotating blades and shaft. Consequently, the torque exerted on both advancing and returning blades and rotor output was successfully simulated. These simulations unveiled the inherently unsteady rotational behavior of the rotor, stemming from the variable torque acting upon the blades. Moreover, the computational results exhibited a notable agreement between the simulated flow pattern around the rotor and the experimental visualization. Furthermore, an approximately identical correlation between the rotor speed and power output was established, mirroring the experimental results. These findings underscore the robust applicability of LBM in facilitating the design and operational analysis of Savonius hydraulic turbines.

INFLUENCE OF THE REDUCING AGENTS ON MORPHOLOGY AND PROPERTIES OF SIVER STRUCTURES ON PAPER

Abstract. A paper-based SERS substrate with tunable plasmonic properties and excellent flexibility has been applied in various fields. These SERS substrates were prepared using a direct chemical reduction synthesis method for the present work. The isotropic and anisotropic morphology of silver structures on cellulose paper fiber were controlled by the types of reducing agents: (1) sodium borohydride (NaBH4), (2) ascorbic acid (L-AA), (3) glucose, (4) formaldehyde (HCHO). The plasmon characterization of silver structures on paper was systematically investigated. The fabricated paper-based SERS substrates were used to detect melamine in an aqueous solution and determine the influence of type reducing agents on the enhancement factor (EF) and signal uniformity of SERS substrates.

Direct Analysis of Marine Dissolved Organic Matter Using LC-FT-ICR MS.

Marine dissolved organic matter (DOM) is an important component of the global carbon cycle, yet its intricate composition and the sea salt matrix pose major challenges for chemical analysis. We introduce a direct injection, reversed-phase liquid chromatography ultrahigh resolution mass spectrometry approach to analyze marine DOM without the need for solid-phase extraction. Effective separation of salt and DOM is achieved with a large chromatographic column and an extended isocratic aqueous step. Postcolumn dilution of the sample flow with buffer-free solvents and implementing a counter gradient reduced salt buildup in the ion source and resulted in excellent repeatability. With this method, over 5,500 unique molecular formulas were detected from just 5.5 nmol carbon in 100 μL of filtered Arctic Ocean seawater. We observed a highly linear detector response for variable sample carbon concentrations and a high robustness against the salt matrix. Compared to solid-phase extracted DOM, our direct injection method demonstrated superior sensitivity for heteroatom-containing DOM. The direct analysis of seawater offers fast and simple sample preparation and avoids fractionation introduced by extraction. The method facilitates studies in environments, where only minimal sample volume is available e.g. in marine sediment pore water, ice cores, or permafrost soil solution. The small volume requirement also supports higher spatial (e.g., in soils) or temporal sample resolution (e.g., in culture experiments). Chromatographic separation adds further chemical information to molecular formulas, enhancing our understanding of marine biogeochemistry, chemodiversity, and ecological processes.

Bioethanol production optimization by direct numerical methods and evolutionary algorithms

Bioethanol production optimization by direct numerical methods and evolutionary algorithms

Interface coupling effect and multi-mode Faraday instabilities in a three-layer fluid system

We investigate the Faraday instabilities of a three-layer fluid system in a cylindrical container containing low-viscosity liquid metal, sodium hydroxide solution and air by establishing the Mathieu equations with considering the viscous model derived by Labrador et al. (J. Phys.: Conf. Ser., vol. 2090, 2021, 012088). The Floquet analysis, asymptotic analysis, direct numerical simulation and experimental method are adopted in the present study. We obtain the dispersion relations and critical oscillation amplitudes of zigzag and varicose modes from the analysis of the Mathieu equations, which agree well with the experimental result. Furthermore, considering the coupling strength of two interfaces, besides zigzag and varicose modes, we find a beating instability mode that contains two primary frequencies, with its average frequency equalling half of the external excitation frequency in the strongly coupled system. In the weakly coupled system, the $A$ -interface instability, $B$ -interface instability and $A$ &amp; $B$ -interface instability are defined. Finally, we obtain a critical wavenumber $k_c$ that can determine the transition from zigzag or varicose modes to the corresponding $A$ -interface or $B$ -interface instability.

Prediction of bridge structure deformation and strain based on dynamic testing and intelligent algorithms

The field load test is a direct and effective method for evaluating the performance of bridge structures. However, existing bridge field static load tests are costly and inefficient; moreover, they obstruct traffic and cause unavoidable damage to the bridge structure. As an alternative the the static load test, a random model update method based on bridge dynamic load tests and the Bayesian inference is proposed in this paper. The bridge static load test results were predicted with a high accuracy. To speed up the Bayesian method to infer the posterior probability density of the updated parameters, the Gaussian process was used in place of the finite element model, and the Bayesian inference used the Markov chain Monte Carlo method based on the delayed rejection adaptive Metropolis algorithm. First, the parameters to be modified for the bridge structure analysis model were determined based on the global sensitivity analysis method. Second, a uniform design sampling method was used to establish the Gaussian process optimization model to update the random model of the bridge structure. Finally, a reinforced concrete truss arch bridge was used to verify the correctness of the static load results of the bridge predicted by the random model update method based on dynamic load testing and Bayesian inference. The research results reveal that the prediction results of the bridge static load test based on the dynamic load test and Bayesian inference method agree with the actual test results.

Numerical study of microscale gas pump based on surface acoustic waves

The concept of microscale fluidic pump based on microchannel with surface acoustic waves (SAWs), propagating along one of its walls, has been extensively studied in the last decade with possible application to lab-on-chip projects. Meanwhile, any mentions of the application of such device to gas medium seem absent in the literature. The present paper aims to fill this gap by investigating the possibility of using microchannel with SAWs as a microscale gas pump. The numerical study is performed using the modification of the direct simulation Monte Carlo method. It was shown that the pumping effect occurs mainly in the area covered by SAW, while the upper layers of gas are almost still in average. The pumping effect demonstrates weak dependence on gas rarefaction, decreases with the SAW speed, and is lower for a low amplitude to channel height ratios. Finally, it is shown that the propulsion intensity in the open system decreases with a decreasing microchannel height, while the compression ratio in the closed system, on the contrary, increases.

Enhancing biological properties with straightforward deposition of durable heparin/chitosan surface coatings on wettable poly(ε-caprolactone)/Tween-20 electrospun fibers

Poly(ε-caprolactone) (PCL) is a hydrophobic aliphatic polyester commonly used as a biomaterial due to its cytocompatibility and biodegradability. However, PCL-based materials are hydrophobic and susceptible to bacterial adhesion and proliferation. To address these disadvantages, this study proposes a direct and practical method for functionalizing the surface and enhancing the biological properties of electrospun PCL fibers using Tween-20 surfactant, followed by the deposition of polyelectrolyte multilayers (PEMs). Tween-20, a neutral and cytocompatible surfactant, was directly incorporated into the fibers without prior surface modifications. The surfactant significantly increased the wettability of the PCL fibers, resulting in water contact angles of 0 degrees, facilitating subsequent functionalization with heparin and chitosan PEMs (with 10, 20, and 30 layers). The materials were characterized through scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), water contact angle and mechanical measurements, Fourier transform infrared-attenuated total reflectance (FTIR-ATR) spectroscopy, X-ray photoelectron spectroscopy (XPS), and atomic force microscopy (AFM). The PEMs remain stable on the fiber surfaces for up to 14 days in a buffer at 37°C. The samples supported high cell viability with no observed cytotoxicity toward human adipose-derived stem cells and no hemolytic activity toward human red blood cells. Antibacterial assays (live/dead and proliferation tests) demonstrated that the coated PCL fibers inhibited the proliferation of Staphylococcus aureus (S. aureus) and Pseudomonas aeruginosa (P. aeruginosa). The PEM coatings significantly enhanced the antimicrobial activity, resulting in a high number of damaged and dead bacterial cells attached to the coated fibers. Therefore, the heparin/chitosan PEMs on Tween-20-modified PCL fibers can prevent bacterial infections while supporting viable mammalian cells.

Synthesis and Characterization of Hexathiosianatoiron(II) [Fe(NCS)6] 4- Complex Compound and Tetrapyridindithiosianatoiron(II) [Fe(NCS)2(Py)4] Complex Compound

This research aims to synthesize and characterize the complex compounds Hexathiocyanatoiron(II) [Fe(NCS)6] 4- and Tetrapyridinedithiocyanatoiron(II) [Fe(NCS)2(Py)4]. The synthesis of these complex compounds was conducted in the Chemistry Laboratory, Faculty of Mathematics and Natural Sciences, Medan State University, while magnetic susceptibility measurements were carried out using the Magnetic Susceptibility Balance (MSB) instrument in the Inorganic Chemistry Laboratory, Bandung Institute of Technology. The complex compound synthesis involved the reaction of Iron(II) sulfate heptahydrate with Potassium thiocyanate and Pyridine compound in a 1:2 and 1:2:4 molar ratio. The synthesis process was conducted through a direct reaction method in a polar solvent. The obtained results include Hexathiocyanatoiron(II) complex compound, [Fe(NCS)6] 4-, which is pale yellow crystalline with a yield of 32.76%, and Tetrapyridinedithiocyanatoiron(II) complex compound, [Fe(NCS)2(Py)4] which is dark purple crystalline with a yield of 52.62%. These compounds exhibit a melting point of 219℃. Magnetic susceptibility measurements using the MSB instrument revealed a magnetic susceptibility of 6.69 BM (paramagnetic) for the Hexathiocyanatoiron(II) complex[Fe(NCS)6] 4-, and a magnetic moment of 5.61 BM (diamagnetic) for the Tetrapyridinedithiocyanatoiron(II ) complex [Fe(NCS)2(Py)4].

Research on the upgrade and maintenance method of apple soluble solids content models

AbstractIn this paper, 3 years (2020, 2021, and 2022) of Bingtangxin apples were used to study the upgrade and maintenance method of apple soluble solids content (SSC) model using near infrared (NIR) spectroscopy. Modeling by partial least squares (PLS) and one‐dimensional convolutional neural network (1D‐CNN) algorithm. The “base model” is built with apples in 2020 and then upgrade and maintain it. The upgraded model is used to predict the sample in 2021 and 2022. Three methods were used to upgrade and maintenance the model, which are based on updated data, direct correction‐based method, and similar band‐based method. Among the three upgrade methods, the model maintenance method using updated data had the greatest improvement in prediction correlation coefficients (Rp) in both years, and both obtained good predictions. The use of direct correction resulted in the greatest reduction in the root mean square error of prediction (RMSEP) in both years. However, the Rp of the model is not improved much by using the direct correction method. The model maintenance method using similar bands combined with different modeling methods also gives good prediction results. Finally, after comparison, the model maintenance method of selecting similar bands is preferred. In the case of limited improvement in the effect of using the similar band method, the model is maintained using the method of adding updated data. The investigation of model upgrade and maintenance methods can reduce human and material consumption and make apple SSC models more versatile.Practical applicationsNIR technology is the first choice for online fruit quality inspection, but its inspection process requires constant upgrading and maintenance with models. The quality of apples varies from year to year, and the model built with samples from a single year does not work well in predicting samples from other years, resulting in the need to upgrade and maintain the fruit quality detection model. However, sometimes, although a large amount of experimental material is lost, it still results in a generic calibration model that is too cumbersome and leads to poor prediction results. At this time, finding an effective model upgrade and maintenance method can both reduce the sample consumption and make the model prediction accuracy improve, and also reduce the model complexity and make the maintained model more robust.

Precursor influence on the raman spectrum of KNNLiTaLa0.01 prepared by two different methods

This work presents a study of ceramics with the (K0.44Na0.52Li0.04)0.97La0.01Nb0.9Ta0.1O3 composition through their Raman spectra. In the previous studies [1], this particular composition has shown good permittivity and piezoelectric parameter values that justify a deeper investigation. Two sets of samples of these ceramic compounds were prepared using different methods. The first set was prepared using a classical solid-state reaction of oxides and carbonates, as described in Ref. 2. The second set was prepared using the NaNbO3 as a precursor. The Raman spectra of the samples obtained by the direct ceramic method consisted of 4 modes. In contrast, the spectra of the samples obtained using the precursor showed a shift of the wavenumber of the A1g(ν1) mode peak towards lower values and consisted of six modes with the appearance of two additional IR modes, F1u(ν3) and F2g(ν4). Notably, the use of the precursor preparation route led to important improvements resulting in a more straightforward method than that of Saito et al. [3].

Rigid-flexible coupling poly (phenylene sulfide) fiber membrane: a highly stable chemical and thermal material for energy and environmental applications

The poly (phenylene sulfide) (PPS) fiber membrane is composed of interwoven fibers, with a three-dimensional porous structure. The three-dimensional porous structure makes PPS fiber membranes have high porosity and large specific surface area, which stands out in the field of membrane separation. A PPS fiber is a high-performance fiber with excellent chemical and thermal stability. These characteristics allow PPS fiber membranes to be used in harsh membrane separation environments such as strong acids, alkalis, and high temperatures. However, the corrosion resistance and high-temperature stability of PPS fibers also make the preparation of PPS fibers and their membranes challenging. In this paper, the preparation method is summarized, including two direct methods to make a PPS fiber membrane: melt-blown spinning and melt electrostatic spinning, and two indirect methods: wet papermaking and weaving. Additionally, the applications of PPS fiber membranes are summarized in detail in energy and environmental fields, such as lithium-ion batteries, alkaline water electrolysis, air filtrations, chemical catalyst substrates, and oil-water separations. This review provides an insightful understanding of PPS fiber membrane preparation methods and the interconnections between these preparation methods and specific applications, thus laying a solid foundation for further advancing the range of PPS fiber membrane applications.

Hyperspectral sparse fusion using adaptive total variation regularization and superpixel-based weighted nuclear norm

Many recent studies have shown that the adaptive total variation regularization has the advantage of better preserving local features of images compared with the celebrated total variation regularization. On the other hand, the superpixel-based weighted nuclear norm can compensate for the shortcomings of the superpixel-based standard nuclear norm, assigning different weights to singular values and improving flexibility. Inspired by these two factors, we propose two new hyperspectral sparse fusion models related to the adaptive total variation regularization and superpixel-based weighted nuclear norm. Furthermore, we design the alternating direction method of multipliers (ADMM) to efficiently solve the proposed models, with complexity and convergence analyses. Experimental results demonstrate that the proposed methods outperform several state-of-the-art methods both numerically and visually.

Augmented Sparse Representation for Incomplete Multiview Clustering.

Incomplete multiview data are collected from multiple sources or characterized by multiple modalities, where the features of some samples or some views may be missing. Incomplete multiview clustering (IMVC) aims to partition the data into different groups by taking full advantage of the complementary information from multiple incomplete views. Most existing methods based on matrix factorization or subspace learning attempt to recover the missing views or perform imputation of the missing features to improve clustering performance. However, this problem is intractable due to a lack of prior knowledge, e.g., label information or data distribution, especially when the missing views or features are completely damaged. In this article, we proposed an augmented sparse representation (ASR) method for IMVC. We first introduce a discriminative sparse representation learning (DSRL) model, which learns the sparse representations of multiple views as applied to measure the similarity of the existing features. The DSRL model explores complementary and consistent information by integrating the sparse regularization item and a consensus regularization item, respectively. Simultaneously, it learns a discriminative dictionary from the original samples. The sparsity constrained optimization problem in the DSRL model can be efficiently solved by the alternating direction method of multipliers (ADMM). Then, we present a similarity fusion scheme, namely, a sparsity augmented fusion of sparse representations, to obtain a sparsity augmented similarity matrix across different views for spectral clustering. Experimental results on several datasets demonstrate the effectiveness of the proposed ASR method for IMVC.

Spectral clustering with linear embedding: A discrete clustering method for large-scale data

In recent decades, spectral clustering has found widespread applications in various real-world scenarios, showcasing its effectiveness. Traditional spectral clustering typically follows a two-step procedure to address the optimization problem. However, this approach may result in substantial information loss and performance decline. Furthermore, the eigenvalue decomposition, a key step in spectral clustering, entails cubic computational complexity. This paper incorporates linear embedding into the objective function of spectral clustering and proposes a direct method to solve the indicator matrix. Moreover, our method achieves a linear time complexity with respect to the input data size. Our method, referred to as Spectral Clustering with Linear Embedding (SCLE), achieves a direct and efficient solution and naturally handles out-of-sample data. SCLE initiates the process with balanced and hierarchical K-means, effectively partitioning the input data into balanced clusters. After generating anchors, we compute a similarity matrix based on the distances between the input data points and the generated anchors. In contrast to the conventional two-step spectral clustering approach, we directly solve the cluster indicator matrix at a linear time complexity. Extensive experiments across multiple datasets underscore the efficiency and effectiveness of our proposed SCLE method.