Classical Identification Methods Research Articles

Audio Compensation with Cascade Biquad Filters for Feedback Active Noise Control Headphones

In active noise control (ANC) headphones, the audio signal is modified together with the primary noise if a feedback controller is used. Although this problem can be alleviated with an FIR model of the secondary path, practical implementations are usually restricted by its computational complexity. In this paper, cascade biquad filters are used to compensate for the modification of the audio system. Instead of using classical identification methods with an IIR model, the audio compensation problem is fixed through an optimization process. An objective function evaluating the comprehensive compensation performance is proposed, whose minimum value is obtained using the differential evolution (DE) algorithm. Simulations and experiments are carried out, whose results validate the effectiveness and efficiency of the proposed optimization method. The averaged compensation error can be reduced to about 0.5 dB with only two to five biquad filters.

One Day in Denmark: Nationwide point-prevalence survey of human bacterial isolates and comparison of classical and whole-genome sequence-based species identification methods.

ObjectivesImplementing whole-genome sequencing (WGS) technologies in clinical microbiology laboratories can increase the amount and quality of information available for healthcare practitioners. In this study, we analysed the applicability of this method and determined the distribution of bacterial species processed in clinical settings in Denmark.MethodsWe performed a point-prevalence study of all bacterial isolates (n = 2,009) processed and reported in the Clinical Microbiology Laboratories in Denmark in one day in January 2018. We compared species identification as performed by classical methods (MALDI-TOF) and by bioinformatics analysis (KmerFinder and rMLST) of WGS (Illumina NextSeq) data. We compared the national point-prevalence of bacterial isolates observed in clinical settings with the research attention given to those same genera in scientific literature.ResultsThe most prevalent bacterium was Escherichia coli isolated from urine (n = 646), followed by Staphylococcus spp. from skin or soft tissues (n = 197). The distribution of bacterial species throughout the country was not homogeneous. We observed concordance of species identification for all methods in 95.7% (n = 1,919) of isolates, furthermore obtaining concordance for 99.7% (n = 1,999) at genus level. The number of scientific publications in the country did not correlate with the number of bacterial isolates of each genera analysed in this study.ConclusionsWGS technologies have the potential to be applied in clinical settings for routine diagnostics purposes. This study also showed that bioinformatics databases should be continuously improved and results from local point-prevalence surveys should not be applied at national levels without previously determining possible regional variations.

MIMO system identification using common denominator and numerators with known degrees

SummaryIn system identification, prior knowledge about the model structure may be available. However, imposing this structure on the identified model may be nontrivial. A new discrete‐time linear time‐invariant identification method is presented in the article that imposes prior knowledge of the degree of the common denominator of the system's transfer function matrix and the degrees of the numerators. First, a method is outlined for the solution in case of exact data. Then, this method is extended for noisy data in the output error setting. An initial estimate obtained by a subspace method is improved by a structured low‐rank approximation method. The performance of the method imposing the structure is compared on simulated data with the performance of classical identification methods that do not impose the structure.

Molecular and phenotypic reidentification of Sporothrix schenckii clinical isolates preserved under mineral oil for 34 to 64 years in a culture collection in Brazil.

Sporotrichosis is a subcutaneous mycosis worldwide distributed reaching hyperendemic proportions in Brazil. Many isolates from patients with sporotrichosis are preserved in culture collections by different methods around the world. The preservation methods are used to maintain the viability and the morphophysiological and genetic characteristics of isolates for long periods. In this study, we evaluated 34 isolates, previously, identified as S. schenckii by a classical identification method, initially preserved by periodical subcultures and then under mineral oil at culture collection of Oswaldo Cruz Institute/Fiocruz, to re-identify them by polyphasic identification. Our results showed that seven isolates remained viable for 34 to 64 years under oil, one isolate lost the ability to sporulate which was reverted by using a medium culture supplemented with rosebush branches and all of them were identified as Sporothrix schenckii sensu stricto by morphological, physiological, partial β-tubulin gene sequencing and phylogenetic analysis.

A New Method for the Parametric Identification of DC Machines Using MATLAB Identification Toolbox and Experimental Measurements

This paper proposes a new approach for the identification of a DC machine (DCM) parameters to build a mathematical model considering different dynamic regimes, which characterize the operation of the studied machine. The proposed solution is simple and is based on the combination of classical identification methods and those available in the identification toolbox of MATLAB. The results obtained experimentally are significantly better and clearly show that the proposed approach is simple to implement and the DCM model is obtained quickly with a reasonable accuracy.

Characteristic and Otopathogenic Analysis of a Vibrio alginolyticus Strain Responsible for Chronic Otitis Externa in China.

Vibrio alginolyticus, a Gram-negative rod bacterium found in marine environments, is known to cause opportunistic infections in humans, including ear infections, which can be difficult to diagnose. We investigated the microbiological and otopathogenic characteristics of a V. alginolyticus strain isolated from an ear exudate specimen obtained from a patient with chronic otitis externa to provide a basis for the future diagnosis of V. alginolyticus-associated infections. The identification of V. alginolyticus was accomplished using a combination of matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS), classical biochemical identification methods, and the use of Vibrio-selective media and advanced molecular identification methodologies. Antimicrobial susceptibility testing revealed that the strain was resistant to ampicillin and sensitive to β-lactam, aminoglycosides, fluoroquinolones, and sulfonamide antibiotics. The potential otopathogenic effects of V. alginolyticus were determined through the performance of cell viability, cell apoptosis, and cell death assays in tympanic membrane (TM) keratinocytes and HEI-OC1 cells treated with V. alginolyticus-conditioned medium using cell-counting kit (CCK)-8 assay, a wound-healing migration assay, Annexin V/propidium iodide (PI) flow cytometric analysis, and terminal deoxynucleotidyl transferase deoxyuridine triphosphate (dUTP) nick-end labeling (TUNEL staining). The results indicated that the identified V. alginolyticus strain exerts cytotoxic effects on keratinocytes and HEI-OC1 cells by inhibiting cell proliferation and migration and inducing apoptosis and cell death. To evaluate the ototoxicity of V. alginolyticus, the cell density and morphological integrity of hair cells (HCs) and spiral ganglion neurons (SGNs) were analyzed after exposing cochlear organotypic explants to the bacterial supernatant, which revealed the pre-dominant susceptibility and vulnerability of HCs and SGNs in the basal cochlear region to the ototoxic insults exerted by V. alginolyticus. Our investigation highlights the challenges associated with the identification and characteristic analysis of the Vibrio strain isolated in this case and ultimately aims to increase the understanding and awareness of clinicians and microbiologists for the improved diagnosis of V. alginolyticus-associated ear infections and the recognition of its potential otopathogenic and ototoxic effects.

An analytical formula to identify the parameters of the energy-based hysteresis model

Energy-based models (also called Vector Play models) are a class of phenomenological models of magnetic hysteresis. In this work an original identification method is presented. The main advantage of this method is its speed and its robustness. Conversely to optimization-based methods, which are prone to convergence problems, the new method is based on analytical formulas. The method is tested experimentally with three different materials (a ferrite and two non oriented steels), and provide an accuracy comparable to classical identification methods, with a much lower computational cost.

Application of adaptive chirp mode decomposition on parameter identification of sub/super‐synchronous oscillation signals

Identifying the parameters of sub/super-synchronous oscillation (SubSO/SupSO) signals is challenging, because (1) the mode numbers of the oscillation signals are unknown, and (2) the oscillation signals are non-stationary and noisy. The non-stationary components need to be accurately identified in order to efficiently suppress SubSO/SupSO. Here, an adaptive chirp mode decomposition (ACMD) method joining a synchroextracting transform (SET) is exploited. First, for detecting the initial instantaneous frequencies (IFs) of SubSO and SupSO, SET is employed to provide time–frequency representations with satisfied energy concentration to ACMD. Second, ACMD is applied to decomposing the main components from the oscillation signal without needing to use the mode numbers, and directly compute the final IFs and instantaneous amplitudes (IAs) for SubSO and SupSO without Hilbert transform. And third, the damping factors of the SubSO and SupSO are further identified with the IAs. The proposed method is evaluated over synthetic signals, simulation data, and real field data from a wind system. Experimental results demonstrate that, compared with the classical signal decomposition-based identification methods, the proposed method more accurately detects frequencies, amplitudes, and damping factors of SubSO and SupSO components for non-stationary and noisy signals.

Study of the biochemical properties of bacteria of the genus Lactobacillus and their identification

This article discusses the current data on the biochemical properties of bacteria of the genus Lactobacillus and their use in industry, particularly in dairy production and biotechnology. Microorganisms of the genus Lactobacillus have antagonistic activity against pathogenic microorganisms and perform an immunomodulatory function. The positive effect of lactobacilli on human health explains their active use in probiotics. The positive effects of normal intestinal microflora and probiotics are mainly due to bifidobacteria and lactobacilli. In probiotic therapy, various types of bacteria of the genus Lactobacillus are used, such as: L. acidophilus, L. rhamnosus, L. plantarum, L. fermentum, L. delbrueckii subsp. bulgaricus, L. casei, L. paracasei. In recent years, the biotechnology of probiotics has been intensively developing - drugs used for the correction and prevention of microecological disorders in the gastrointestinal tract of humans and animals. An urgent issue is obtaining new data on the biological properties of lactobacilli, creating new probiotic preparations based on them using modified approaches to cultivation. The results of determining the species L. acidophilus L. casei, L. rhamnosus, L. paracasei, obtained by the classical biochemical identification method based on saccharolytic activity, complicating species identification, are comparable to the molecular genetic method. In the case of L. casei, L. rhamnosus, L. paracasei, it is necessary to carry out modern identification methods based on the polymerase chain reaction, since their biochemical properties are similar, which makes it difficult to carry out species identification. The molecular genetic method is a valuable addition to the intergeneric and species identification of lactobacilli, given the variability of the classical biochemical method. Key words: Lactobacillus, biochemical identification, probiotics, bacteria.

Genetic algorithm application for electrodynamic transducer model identification

Research object: the adaptation and application of the genetic algorithm for electrodynamic transducer model parameters identification.
 Investigated problem: to formulate loudspeaker identification task as an optimization problem, adapt it to the genetic algorithm framework and compare obtained results with classical identification method using added mass.
 Main scientific results: the complete genetic algorithm loudspeaker identification procedure is presented, including:
 – data acquisition scheme, where the directly measured values for the algorithm application are: voltage at loudspeaker terminals, current through the voice coil and displacement of the moving part
 – selection of an appropriate set of genes of an individual
 – derivation of the fitness function for assessing the quality of the identified parameters, which can also be used to identify other types of electroacoustic transducers
 Also, the advantages of this method in comparison with the classical method of identification using added mass are considered, that are its versatility and ability to quickly configure and adapt for research and experimentation with different loudspeaker models and different types of transducers used in acoustics.
 Area of practical use of the research results: the proposed genetic loudspeaker model identification scheme can be directly applied on practice to speed up research and development tasks in electroacoustics and other related fields that require frequent experimentation with different types of transducer models.
 Innovative technological product: genetic algorithm based loudspeaker identification scheme that can be applied to identify various model of electrodynamic transducers.
 Scope of application of the innovative technological product: electroacoustics, loudspeaker design, audio systems

Investigation of the Possibility of Applying a Genetic Algorithm for Electroacoustics Problems

This paper considers the adaptation and application of a genetic algorithm to find the parameters of the electrodynamic transducer model. The advantages and disadvantages of this method in comparison with the classical method of identification using added mass are considered. The derivation of the suitability function for estimating the identified parameters is presented, which can also be used to identify other types of electroacoustic transducers. The theory underlying genetic algorithms has been examined and shown how genetic algorithms work by assembling the best solutions from small structural elements with excellent qualities. Next, the differences between genetic and traditional algorithms were analyzed, including population population support and the use of genetic representation of solutions. After that, the strengths of genetic algorithms were described, including the possibility of global optimization and applicability to problems with complex mathematical representation or without representation at all, and noise resistance. Disadvantages were also highlighted: the need for special definitions and settings of hyperparameters, the danger of premature convergence. In conclusion, the situations when the use of genetic algorithms are listed This algorithm is not tied to a specific engineering or scientific field, which makes it universal, it is equally used in genetics and computer science. The parameters were determined using a genetic algorithm and compared with the more classical method of added mass for acoustics. The comparative table in the work illustrates the high accuracy of the genetic algorithm in comparison with the method of added mass. During the work on the practical part, also to improve the behavior of the model at frequencies higher than the resonant, it was decided to complicate the model of the electrical subsystem of the tranducer and introduce additional parameters: parallel resistance and parallel inductance. As a result, the complicated model began to correspond better to the measured values in the entire frequency domain, and is therefore more accurate. This is an example of the convenience of using a genetic algorithm in the transition from identification of one model with specific parameters to another. The results of this work prove that the use of a genetic algorithm is appropriate for solving electroacoustic problems because its application allows to quickly experiment and identify more complex models for which the added mass method can not be applied. Also, in the future, genetic algorithm can be used to identify transducer models of in time domain, for example, nonlinear models of electrodynamic transducers or models in a state space, which is the subject of future research.This paper considers the adaptation and application of a genetic algorithm to find the parameters of the electrodynamic transducer model. The advantages and disadvantages of this method in comparison with the classical method of identification using added mass and the method of parameter selection BL are considered. The derivation of the fitness function for assessing the quality of the identified parameters is presented, which can also be used to identify other types of electroacoustic transducers. The directly measured values ​​for the application of the algorithm are the voltage at the terminals of the converter, the current through the coil of the converter and the displacement of the moving part of the converter. The undoubted advantage of the genetic algorithm compared to classical identification methods is its versatility and the ability to quickly adapt and configure for research and experimentation with different models and different types of transducers used in acoustics. This article describes the adaptation and application of a genetic algorithm to find parameters of an electrodynamic transducer model. The advantages and disadvantages of this method in comparison with the classical identification method using added mass are considered. The derivation of the fitness function for assessing quality of the identified parameters is presented, which can also be used to identify other types of electroacoustic transducer models.

Physics-constrained deep learning of multi-zone building thermal dynamics

We present a physics-constrained deep learning method to develop control-oriented models of building thermal dynamics. The proposed method uses systematic encoding of physics-based prior knowledge into a structured recurrent neural architecture. Specifically, our method incorporates structural prior knowledge from traditional physics-based building modeling into the architecture of the deep neural network model. Further, we also use penalty methods to provide inequality constraints, thereby bounding predictions within physically realistic and safe operating ranges. We observe that stable eigenvalues accurately characterize the dissipativeness of the system, and use a constrained matrix parameterization based on the Perron-Frobenius theorem to bound the dominant eigenvalues of the building thermal model parameter matrices. We demonstrate the effectiveness and physical interpretability of the proposed data-driven modeling approach on a real-world dataset obtained from an office building with 20 thermal zones. The proposed data-driven method can learn interpretable dynamical models that achieve high accuracy and generalization over long-term prediction horizons. We show that using only 10 days’ measurements for training, our method is capable of generalizing over 20 consecutive days. We demonstrate that the proposed modeling methodology is achieving state-of-the-art performance by significantly improving the accuracy and generalization compared to classical system identification methods and prior advanced methods reported in the literature. compared to prior state-of-the-art methods reported in the literature.

Discrimination of raw material species in mixed seafood products (surimi) using the next generation sequencing method

The increased international trade and worldwide consumption of seafood has increased the possibility of economic fraud. Particularly, the increased development of processed seafood has restricted the application of classical identification methods. DNA matabarcoding is a rapid method for identification of multiple species from an environmental sample. In this study, two universal markers were designed in the mitochondrial cytochrome oxidase subunit I region, and the metabarcoding analysis of processed seafood products was done using these markers. A total of 11 seafood products were analyzed, and 56 and 32 species within 36 families between them were identified by each primer set, respectively. Comparing the identified species with each primer set, only 12.8% of the species were identified by both primer sets. In addition, the species on the label when identified were not observed in some products. Although the analysis was done on the same gene region, different results can be obtained according to the primer set. Based on these results, it is advantageous to use more than one primer set to identify various species from seafood products using DNA metabarcoding analysis.

Mistuning identification for coated blisks using small amount of experimental data

Because the coatings are likely to produce new mistuning which aggravates the vibration failure of coated blades, it is particularly important to study the mistuning identification for coated blades. In this study, the degrees of freedom of the whole disk of the coated blisk is completely constrained, and the orders of the dynamic model of the coated blisk based on component mode mistuning (CMM) method is further reduced. Based on this model, a novel mistuning identification method for the coated blade of the coated blisk by small amount of experimental data is proposed, which can identify the mass, stiffness and damping mistuning of the blade substrates and coatings. The input parameters with high amount of data, such as modal shape matrix, are removed from identification formulas, thus, both the amount of data for identification calculation and the workload of vibration testing are reduced. The mistuning identifications of the numerical case and actual case are carried out respectively, and the identification process of the mistuning identification method in this paper is compared with the one based on the classical CMM model. It is shown that the mistuning identification method is effective and feasible, and the identification efficiency is higher than the classical CMM identification method.

Genetic Characterization of Some Cyanobacterial Species Using by RAPD-PCR Technique

Cyanobacteria are the ecologically important organisms due to they thrive in wide variety of habitats and some with the ability to produce secondary metabolites known as cyanotoxin. Besides classical identification methods, DNA fingerprint and many other PCR based molecular approaches have being adopted and well used in the molecular systematics and genetic characterization of Cyanobacteria. In this study, 7 bacterial isolates were analyzed through the RAPD-PCR technique using 15 different randomly selected primers. All the bacterial isolates were produced 306 completely polymorphic amplification products. A minimum 12 and maximum 35 different PCR fragments were obtained by the OPA-14 and Hip-GC primers respectively. The size of RAPD-PCR fragments were varied between 175-3.000 bp. Two different dendrogrames (UPGMA ve Neighbor Joining) were created using Nei’s genetic distance and similarity values, to identify genetic relationships among analyzed bacterial isolates. According to the results, the Synechococcus elongatus and Pseudoanabaena moniliformis are defined as the most distant species, while the Anabaena affisinis and Arthrospira sp. are determined as the closest organisms. Consequently, randomly selected primers have generated 100% polymorphic fragments, indicating high efficiency and successful utilization of RAPD-PCR technique in the genetic characterizations and molecular systematics of different Cyanobacteria isolates. Keywords: Cyanobacteria, genetic characterization, DNA polymorphism, RAPD-PCR technique. DOI: 10.7176/JSTR/6-09-09

Identification and Characterization of Novel Thermophilic Bacteria from Hot Springs, Erzurum, Turkey.

The aim of present study was the isolation and characterization of thermophilic bacteria from three different hot springs in Erzurum, Turkey. For this purpose, 85 bacteria were isolated and characterized by ERIC-PCR genomic fingerprinting and classical identification methods such as morphological, physiological and biochemical characteristics. According to the results, 29 bacterial isolates with different band profiles were analyzed by 16S rRNA gene sequencing and identified as belonging to the genus of Bacillus, Pseudomonas, Silanimonas, Thermomonas and Thauera. This is the first report on the isolation of Silanimonas lenta, Thauera sp. and Thermomonas haemolytica from Turkey Hot Springs. The amylase, lipase and protease enzyme production potentials of the isolates were 80%, 91.25% and 81.25%, respectively. Moreover, 56.47% of the isolates (48) were able to produce all of these enzymes. Therefore, the results of the study indicated that these bacteria and their enzymes can be used as a source of industrial enzymes.

An Improved Selective Ensemble Learning Method for Highway Traffic Flow State Identification

Reliable and accurate real-time traffic flow state identification is crucial for an intelligent transportation system (ITS). This identification is a prerequisite for alleviating traffic congestion and improving highway operation efficiency. In this paper, we propose an improved traffic flow state identification model that is based on selective ensemble learning (SEL). First, we adopted the fuzzy C-means (FCM) clustering method to divide the traffic flow data into three main kinds of traffic flow states and obtained the parameters that correspond to each kind of traffic flow state. Second, we applied the random subspace (RS) algorithm as the ensemble method and support vector machine (SVM) model as base learners to construct the RS-SVM ensemble model for traffic flow identification. Significantly, the discrete binary particle swarm optimization (BPSO) algorithm with global optimization search ability was employed to select the classifiers obtained by the random subspace training in the ensemble system. We experimentally validated the effectiveness of the proposed BPSO–RS-SVM-SEL approach. The research results reveal that compared with other classical traffic flow state identification methods, the proposed model has a higher maximum accuracy of 98.68%. It can be seen that our model improves the classification accuracy of traffic flow state identification and the difference in the ensemble system to a certain extent.

An overview on Taxonomic Keys and Automated Species Identification (ASI)

A core goal of taxonomy and systematics, entomology, field botany, horticulture, zoology and many agriculture courses involves learning to identify plants and animals. But current syllabi and time constraint allow students to see only a limited amount of taxonomic variability. Usually only experts such as taxonomists and trained technicians can identify taxa accurately because it requires special skills acquired through extensive experience. Taxonomic keys are essential tools for species identification, used by students and professionals. The development of computer-based, multi-media keys provides one means of addressing this critical identification and diagnostic function. Automated species identification (ASI) is a method of making the expertise of taxonomists available to ecologists, parataxonomists and others via digital technology and artificial intelligence. Today, most automated identification systems rely on images depicting the species for the identification. Although computer programs will not replace classical plant identification methods, they have the potential to make these methods more effective.

Distributed Online Load Sensitivity Identification by Smart Transformer and Industrial Metering

Load power sensitivity to voltage changes continuously in the distribution grid due to the increased variability of the load demand (e.g., electric vehicles charging) and generation production (e.g., photovoltaic). Classical sensitivity identification methods do not respect the fast dynamics of such changes: they require long data history and/or high computational power to update the load sensitivity. The proposed online load sensitivity identification (OLLI) approach is able to identify the load sensitivity in real time (e.g., every minute). This paper demonstrates that the OLLI can be achieved not only with the advanced smart transformer metering system but also with commercial industrial metering products. It is shown that OLLI is able to identify correctly the load sensitivity also in the presence of noise or fast stochastic variation of power consumption. The industrial metering-based OLLI application has been proven by means of a power-hardware-in-loop evaluation applied on an experimental microgrid.

Inertia parameter identification of anunknown captured space target

Purpose The purpose of this paper is to present a method to obtain the inertia parameter of a captured unknown space target. Design/methodology/approach An inertia parameter identification method is proposed in the post-capture scenario in this paper. This method is to resolve parameter identification with two steps: coarse estimation and precise estimation. In the coarse estimation step, all the robot arms are fixed and inertia tensor of the combined system is first calculated by the angular momentum conservation equation of the system. Then, inertia parameters of the unknown target are estimated using the least square method. Second, in the precise estimation step, the robot arms are controlled to move and then inertia parameters are once again estimated by optimization method. In the process of optimization, the coarse estimation results are used as an initial value. Findings Numerical simulation results prove that the method presented in this paper is effective for identifying the inertia parameter of a captured unknown target. Practical implications The presented method can also be applied to identify the inertia parameter of space robot. Originality/value In the classic momentum-based identification method, the linear momentum and angular momentum of system, both considered to be conserved, are used to identify the parameter of system. If the elliptical orbit in space is considered, the conservation of linear momentum is wrong. In this paper, an identification based on the conservation of angular momentum and dynamics is presented. Compared with the classic momentum-based method, this method can get a more accurate identification result.