Classical Identification Methods Research Articles

Chlamydia and Vaginitis in Sexually Active Females: Classical Identification Methods for Effective Control

Laboratory diagnosis of Chlamydia and vaginitis in sexually active females has been limited by unavailability of a sequential method/rapid technique for simple diagnosis. Six hundred (600) adult females from hotel/brothel, Sexually Transmitted Infections (STIs) Clinic, Obstetrics/Gynaecology Clinic, Family Planning Clinic and Healthy controls were investigated for Chlamydia, Candida, trichomoniasis and bacterial vaginosis (BV). This was done using microscopy: wet mount, stained vaginal secretion and stained smear after culture. Results showed that there were 72% infections in the female groups. The brothel and STI group had infection in the range (70-86%). Chlamydial infection was highest in the STI group while Candida infection was highest in the healthy (control) females. Bacterial vaginosis was distributed in all groups. As p-value increased, f-value increased indicating constant co-infection of Candida and BV in Chlamydia positive females. Microscopy by direct detection from sample and stained smear after culture were in the range: 56-86%. Direct microscopy for BV was 78.5% and stained smear after culture, 57.1%. Sensitivity and specificity of the techniques showed that detection of Chlamydia was less sensitive by direct microscopy of sample but sensitivity and specificity of stained smear after culture were high. Immunoassay (32.2%) was also less sensitive. Sensitivity and specificity of wet mount microscopy for Candida, Trichomoniasis and BV were in the range 62.5 – 80% and 62.5-97.8% respectively. Wet mount has high sensitivity and specificity for detecting agents of vaginitis and may be useful for routine use and for diagnosis where disease is absent, thus, making identification more cost effective.

A Classical Parameter Identification Method and a Modern Test Generation Algorithm

Many methods have been presented for the testing and diagnosis of analog circuits. Each of these methods has its advantages and disadvantages. In this paper we propose a novel sensitivity analysis algorithm for the classical parameter identification method and a continuous fault model for the modern test generation algorithm, and we compare the characteristics of these methods. At present, parameter identification based on the component connection model (CCM) cannot ensure that the diagnostic equation is optimal. The sensitivity analysis algorithm proposed in this paper can choose the optimal set of trees to construct an optimal CCM diagnostic equation, and enhance the diagnostic precision. But nowadays increasing attention is being paid to test generation algorithms. Most test generation algorithms use a single value in the fault model. But the single values cannot substitute for the actual faults that may occur, because the possible faulty values vary over a continuous range. To solve this problem, this paper presents a continuous fault model for the test generation algorithm which has a continuous range of parameters. The test generation algorithm with this model can improve the treatment of the tolerance problem, including the tolerances of both normal and faulty parameters, and enhance the fault coverage rate. The two methods can be applied in different situations.

Multi-Basis Wavenet-Based Stator Resistance Identification in DTC Induction Motor Systems

The performance of direct torque controlled (DTC) induction motor system is greatly affected by the change of the motor stator resistance especially when the motor runs at low speeds. A new architecture of multi-basis wavenet-based model is proposed and implemented for stator resistance identification. Such multi-basis model utilizes multi-set daughter wavelets. By means of enough training of samples, the descent gradient algorithm is used to fulfill both system structure and parameters initialization, and the stator resistance is then acquired online with the operation of the system. The simulation results were carried out using Matlab/Simulink library and compared with those obtained from classical stator resistance identification method based on PI control and from another recent wavenet-based method. The simulation results show that the proposed method can improve the system performance. Keywords: Wavenet, Multi-basis wavenet, Direct torque control, Stator resistance, Induction motor.

Identificación bacteriana mediante espectrometría de masas matrix-assisted laser desorption ionization time-of-flight. Comparación con la metodología habitual en los laboratorios de Microbiología Clínica

The methods routinely used for bacterial identification in Clinical Microbiology Laboratory, although miniaturized and automated, are still based on the same basic principles as classical identification methods. Nevertheless, technological advances are emerging which could modify these routine methods. We report a comparative study between conventional identification methods and mass spectrometry MALDI-TOF (MS MALDI-TOF) for bacterial identification in the Clinical Microbiology Laboratory. We analysed 294 facultative anaerobic and aerobic isolates (65 Gram positives and 229 Gram negatives), obtained from different clinical samples, using conventional microbiological methods (Wider, Fco. Soria Melguizo, Madrid, Spain; Vitek-2, APIStaph, API 20 Strep, API Coryne and API NH, bioMérieux, Marcy L'Etoile, France) and an Autoflex III MS with a MALDI-TOF device (Bruker Daltonics GmbH, Leipzig, Germany). Salmonella isolates were also typed by using specific sera. Isolates identified with a confidence rate <95% were checked by using API systems. Isolates which were not accurately identified by API systems were rejected. MS MALDI-TOF identification is automatically scored by the system software between 1 and 3 points. Isolates with scores <1.5 were classified as unreliable. Correlation between both identifications was classified as correlation at the species level, at the genus level or no correlation. Correlation at the species level in Gram positives was 100%. Correlation in Gram negatives was 87.7% at the species level and 97.7% at the genus level. There was no correlation in 2.2% of Gram negatives studied. Identification failures occurred in the genera Raoultella and Acinetobacter, in Stenotrophomonas maltophilia and in Francisella tularensis. Bacterial clinical isolates identification obtained by MS MALDI-TOF shows excellent correlation with identification obtained by conventional microbiological methods. Moreover, MS MALDI-TOF allows the identification of bacteria from colonies grown on agar culture plates in just a few minutes, with a very simple methodology and hardly any consumable costs, although the financial costs of purchasing the device can be high.

A new strategy of outlier detection for QSAR/QSPR

The crucial step of building a high performance QSAR/QSPR model is the detection of outliers in the model. Detecting outliers in a multivariate point cloud is not trivial, especially when several outliers coexist in the model. The classical identification methods do not always identify them, because they are based on the sample mean and covariance matrix influenced by the outliers. Moreover, existing methods only lay stress on some type of outliers but not all the outliers. To avoid these problems and detect all kinds of outliers simultaneously, we provide a new strategy based on Monte-Carlo cross-validation, which was termed as the MC method. The MC method inherently provides a feasible way to detect different kinds of outliers by establishment of many cross-predictive models. With the help of the distribution of predictive residuals such obtained, it seems to be able to reduce the risk caused by the masking effect. In addition, a new display is proposed, in which the absolute values of mean value of predictive residuals are plotted versus standard deviations of predictive residuals. The plot divides the data into normal samples, y direction outliers and X direction outliers. Several examples are used to demonstrate the detection ability of MC method through the comparison of different diagnostic methods.

Recurrent auto-associative artificial neural network model of Biomass Steam Boiler System

Abstract In the paper a recurrent auto-associative artificial neural network structure is used to obtain a dynamic model of Biomass steam boiler system. Upon offline real process data a model was derived and results were compared to model derived by classic identification method. A good dynamic model was needed for design, testing and tuning of the Fuzzy controller, which resulted in optimization of steam production. By using recurrent auto-associative neural network instead of locally adequate state space model, more general model with a better fit to the wide range of process data was achieved. Implementation of such complex neural network was tested on typical industrial PLC and compared to Matlab simulation results.

Mycosphaerella dearnessii and Mycosphaerella pini

<i>Mycosphaerella dearnessii</i> and <i>Mycosphaerella pini</i>

Identification for control of multivariable systems: Controller validation and experiment design via LMIs

This paper presents a new controller validation method for linear multivariable time-invariant models. Classical prediction error system identification methods deliver uncertainty regions which are nonstandard in the robust control literature. Our controller validation criterion computes an upper bound for the worst case performance, measured in terms of the H∞-norm of a weighted closed loop transfer matrix, achieved by a given controller over all plants in such uncertainty sets. This upper bound on the worst case performance is computed via an LMI-based optimization problem and is deduced via the separation of graph framework. Our main technical contribution is to derive, within that framework, a very general parametrization for the set of multipliers corresponding to the nonstandard uncertainty regions resulting from PE identification of MIMO systems. The proposed approach also allows for iterative experiment design. The results of this paper are asymptotic in the data length and it is assumed that the model structure is flexible enough to capture the true system.

Affinity purification of a siderophore that exhibits an antagonistic effect against soft rot bacterium

Bacterial colonies were isolated from different Egyptian soil samples. From these isolates, one bacterial species was found to produce siderophore. Using classical and biochemical identification methods, the siderophore producing isolate was identified as Pseudomonas fluorescens. Based on the affinity of siderophores for metal ions, an affinity chromatography system was designed for the purification of the siderophore in one step. It was possible to isolate 25 mg siderophore per liter of culture media. The purified siderophore was found to exist in two forms of approximately 30 and 90 kD. They are believed to be polymers of several siderophore molecules. Both forms were found to be active against the pathogen Erwinia carotovora var. carotovora, the causal bacteria of soft rot disease on potato tubers. The advantage of this method over other purification methods is that it uses metal ion so it can be applied for the purification of the known types of siderophores. Moreover, the purification is based on affinity chromatography, so the siderophore purity state permits several biotechnological applications without further treatments.

Identifying a Transfer Function From a Frequency Response

Abstract In this paper, the classic Levy identification method is reviewed and reformulated using a complex representation. This new formulation addresses the well known bias of the classic method at low frequencies. The formulation is generic, coping with both integer order and fractional order transfer functions. A new algorithm based on a stacked matrix and its pseudoinverse is proposed to accommodate the data over a wide range of frequencies. Several simulation results are presented, together with a real system identification. This system is the Archimedes Wave Swing, a prototype of a device to convert the energy of sea waves into electricity.

NARX model identification with error filtering

Model identification of polynomial NARX models involves a lengthy and computationally intensive procedure for selecting the model structure among a possibly large set of candidate regressors. If the model structure is under-parameterized to reduce the burden of the model selection phase, unsatisfactory results are generally obtained. This inaccuracy problem can be somewhat circumvented by focusing the identification process on the obtainment of an accurate local model over a specific frequency range. Such frequency tailoring is achieved in the nonlinear modeling framework by direct error filtering, as opposed to the data pre-filtering practice adopted in the linear context. This work discusses the application of error filtering to classical NARX model identification methods. A simulation example is provided to show the performance of the proposed approach in deriving accurate local models, despite an under-parameterized model structure. It is also shown that a proper error filtering may increase the model accuracy in simulation with respect to available identification techniques.

Real-time PCR assay for detection and relative quantification of Liocarcinus depurator larvae from plankton samples

Accurate species identification of decapod crustacean larvae is required to understand their population distributions, life cycle dynamics and interactions with their habitats. Analysis of plankton samples using morphological taxonomic methods and microscopy is time-consuming, requires highly skilled and trained operatives and may often be inaccurate. As complementary tools to classical identification methods, recent work has focused on the development of molecular approaches and shows their feasibility for species-specific identification. This study has developed real-time PCR assays utilising species-specific Taqman® probes designed in the cytochrome oxidase I (COI) gene of Liocarcinus depurator, Necora puber, Carcinus maenas and Cancer pagurus. Our study then employed the probe and primers designed for L. depurator to obtain accurate identification and relative abundance estimates of L. depurator larvae in plankton samples collected between March 2005 and October 2006. Ranges of larval abundances were derived from a standard curve created from plankton samples spiked with a known number of larvae reared in the laboratory. Inhibition of the PCR reaction was shown to be an important factor and our results suggested that 0.1 ng of DNA as template provided accurate identification and avoided inhibition. Real-time PCR was shown to provide accurate species identification on unsorted plankton samples and could be suitable for the estimation of larval abundances in the plankton, although more work must be done to improve the accuracy of those estimations.

Output-Only Technique for Parameter Identification of Nonlinear Systems Working under Operational Loads

Vast majority of industrial mechanical systems do not behave in linear way around any operating point and work under non-measurable operational loads. Identification of such systems can’t be carried out by means of the classical nonlinear system identification methods. The paper concerns the method combining restoring force, boundary perturbation and direct parameter estimation techniques that, on the contrary to classical methods, requires neither input measurement nor linear behaviour of the considered system around an operating point. There are also presented results of method application to parameter identification of machine shaft flexible support.

AUV IDENTIFICATION BY USE OF SELF-OSCILLATIONS

Given the fact that AUV dynamics change depending on the payload, finding a mathematical model in a rather small period of time is quite important. Classical openloop identification methods give accurate parameter identification, but are also time consuming. In the paper we present an identification method based on induced self-oscillations, which has proved to be applicable to underwater vehicles. In addition to that, an error analysis for the proposed method is presented. Experimental results obtained on an underwater vehicle are given and compared to the results obtained using open-loop identification algorithms.

RELIABILITY OF PARAMETRIC VARIANCE ESTIMATES FOR IDENTIFIED TRANSFER FUNCTIONS

Classical system identification methods result in an identified parametric plant and noise model. Due to modelling errors, the estimated variance of the plant model can fail completely to give a reasonable idea of the amplitude of the model errors while this is not detected at all in the validation test. A robust method is presented to validate the parametric variance estimate, and to indicate the reliable frequency bands where the estimated variance can be safely used as an indication of the remaining model errors. Outside these bands the model is unreliable (neither validated or invalidated).

Identification of family relationships by epigenetic traits

In analyzing family burials, it is often necessary to establish the nature of the family relationship. This study examines 18 skulls from the 14th and 15th century, presumptively assigned to the family of the Counts of Celje from the territory of present-day Slovenia. Though DNA analysis is the identification method of choice, it is not always possible to apply it. In our case we were unable to isolate the genetic material and had to use classical identification methods. After determining sex and age, we established the kinship by comparison of X-ray images of paranasal cavities (frontal and maxillary sinuses, and also orbital and nasal cavities), the shape and size of which are autosomal dominant inherited characters. The comparison also extends to numerous other likewise inherited epigenetic trait similarities on the skulls. We identified all skulls, compared them with historical and genealogical data, grouped them into nuclear families--one of them is presented in this article-, and proved that they belong to the assigned family. This multidisciplinary work, based on latest epigenetic research, is highly relevant for modern non-genetic identification studies and verification of kinship by skulls collectively interred and can be applied in similar cases where isolation of DNA is no longer possible, even though the skeletal remains may not be old.

Sulfato/thiosulfato reducing bacteria characterization by FT-IR spectroscopy: A new approach to biocorrosion control

Sulfato and Thiosulfato Reducing Bacteria (SRB, TRB) can induce corrosion process on steel immersed in seawater. This phenomenon, called biocorrosion, costs approximatively 5 billion euros in France each year. We provide the first evidence that Fourier Transformed InfraRed (FTIR) spectroscopy is a competitive technique to evaluate the sulfurogen flora involved in biocorrosion in comparison with time consuming classical identification methods or PCR analyses. A great discrimination was obtained between SRB, TRB and some contamination bacteria known to be present in seawater and seem to be able to reduce sulfate under particular conditions. Moreover, this preliminary study demonstrates that FTIR spectroscopic and genotypic results present a good correlation (these results are confirmed by other data obtained before or later, data not shown here). The advantages gained by FTIR spectroscopy are to give information on strain phenotype and bacterial metabolism which are of great importance in corrosion processes.

MODELING OF FRICTIONS IN THE TRANSMISSION ELEMENTS OF A ROBOT AXIS FOR ITS IDENTIFICATION

Control in robotics needs more and more precise models of the mechanical parts of the structure and specially for a complex system such as a biped robot. An important but difficult aspect of this work is the modeling of the mechanical loss due to friction in the chain of transmission from the motor to the axis. Each part losses are defined as a sum of three terms, one constant, another depending only on the speed and the last depending on the torque transmitted. The robot joint kinematic chain is modeled with three elements: the motor, the gearbox and a rotational joint at the leg. The results show a good adequacy between measurement and simulation with the proposed identification method in comparison with a classic least square identification method.

Blind sequential detection for Rayleigh fading channels using hybrid Monte Carlo-recursive identification algorithms

Detection of data transmitted over a Rayleigh fading channel, where the channel is unknown, has been a problem of interest for many researchers. In this paper, we present a new algorithm for joint detection and channel estimation for Rayleigh fading channels. Our algorithm combines Monte Carlo sampling with classical recursive identification methods. The channel is modeled as an autoregressive process, which allows for representation of the communication system by a dynamic state space model. A more accurate modeling of the channel, especially in fast fading along with exploitation of time diversity in the received signal, is also considered. Simulation results illustrating the effectiveness of this algorithm are presented.

Performance Analysis for Subband Identification

The so-called subband identification method has been introduced recently as an alternative method for identification of finite-impulse response systems with large tap sizes. It is known that this method can be more numerically efficient than the classical system identification method. However, no results are available to quantify its advantages. This paper offers a rigorous study of the performance of the subband method. More precisely, we aim to compare the performance of the subband identification method with the classical (fullband) identification method. The comparison is done in terms of the asymptotic residual error, asymptotic convergence rate, and computational cost when the identification is carried out using the prediction error method, and the optimization is done using the least-squares method. It is shown that by properly choosing the filterbanks, the number of parameters in each subband, the number of subbands, and the downsampling factor, the two identification methods can have compatible asymptotic residual errors and convergence rate. However, for applications where a high order model is required, the subband method is more numerically efficient. We study two types of subband identification schemes: one using critical sampling and another one using oversampling. The former is simpler to use and easier to understand, whereas the latter involves more design problems but offers further computational savings.