Effective Identification Algorithm Research Articles

Metaheuristic Optimization Algorithm for Rapid Parameter Identification of the Bouc–Wen Model for Self-Centering Shear Walls

An effective rapid performance evaluation technique for structures is essential for disaster reduction, while it is difficult to realize particularly for complex hysteretic structures. The Bouc–Wen–Baber–Noori model is a versatile smooth hysteretic model that describes the features of stiffness degradation, strength degradation and pinching. However, the identification of these parameters is difficult owing to the lack of an effective identification algorithm for structures with complex hysteretic characteristic. To obtain the hysteretic parameters of the characteristic of self-centering shear walls efficiently, an optimization algorithm called the self-adaptive parallel genetic algorithm (SPGA) was developed based on the cyclical experiment results of seven self-centering shear walls, which are characterized by a very small residual deformation. This study focused on the problems of identification accuracy and efficiency, improving the genetic operators, optimizing the genetic strategies, and concerning about the searching stability, local convergence, and time consumption in high-dimensional and large-scale optimization spaces. The feasibility and superiority of the SPGA were verified by comparing the simulated hysteretic characteristic, lateral forces and stiffness degradation curve with the identification results of the standard genetic algorithm (SGA) and experimental data. A comparison of the convergence, fluctuation, and time consumption between the SPGA and SGA also demonstrates the advantage of the optimized algorithm.

Adaptive Model Recovery Scheme for Multivariable System Using Error Correction Learning

In automatic control design, identifying the system parameters based on an effective identification algorithm is often necessary. Given that many realistic plants are a multivariable system, such identification is considered critical. Most of the available multivariable system identification techniques are designed based on prediction error-correction learning, which produces an unsatisfactory estimation accuracy and low convergence speed, especially in the case of strong external interference. In this article, an adaptive identification scheme is proposed to achieve model recovery for a multivariable system based on a novel error correction learning framework. First, three fictitious sub-models are established by means of the hierarchical principle, in which the high computational burden of the identification approach is avoided. Second, the aforementioned identification method is proposed to recover the parameter information of each sub-model. To improve the identification performance, the identification error information contained in the system data is derived and used to establish a criterion function. According to the identification error and initial parameter error data, a novel criterion function structure relying on the regularization and punishment mechanisms is proposed. Based on this criterion function, a new adaptive error correction learning parameter update law is then deduced. A numerical example and a real-world plant are examined to verify the advantage and practicality of the presented adaptive identification scheme.

Human Gesture Controlled Car Robot

In present era of development and growth, the technology has made possible for people to operate electronic devices more conveniently. Now we are able to operate machines without giving it a touch with the help of technology called Hand Gesture Recognition. Here we have devised a gesture controlled car robot which uses PIC16F738 microcontroller and accelerometer to achieve human computer interaction. In this paper we deal with development and implementation of wireless and an accelerometer based hand gesture controlled car robot using RF transmitter and receiver. Transmitter detects the movement of hand and sends the command to the receiver by RF; receiver receives the command and moves the robot accordingly. Apart from conventional approach of controlling mechanism of car robots via buttons etc., here we have developed a definite and effective algorithm for identification of the gestures.

Extracting inter-area oscillation modes using local measurements and data-driven stochastic subspace technique

In this paper, a data-driven stochastic subspace identification (SSI-DATA) technique is proposed as an advanced stochastic system identification (SSI) to extract the inter-area oscillation modes of a power system from wide-area measurements. For accurate and robust extraction of the modes’ parameters (frequency, damping and mode shape), SSI has already been verified as an effective identification algorithm for output-only modal analysis. The new feature of the proposed SSI-DATA applied to inter-area oscillation modal identification lies in its ability to select the eigenvalue automatically. The effectiveness of the proposed scheme has been fully studied and verified, first using transient stability data generated from the IEEE 16-generator 5-area test system, and then using recorded data from an actual event using a Chinese wide-area measurement system (WAMS) in 2004. The results from the simulated and recorded measurements have validated the reliability and applicability of the SSI-DATA technique in power system low frequency oscillation analysis.

Profiling of Histone Post-Translational Modifications in Mouse Brain with High-Resolution Top-Down Mass Spectrometry.

As histones play central roles in most chromosomal functions including regulation of DNA replication, DNA damage repair, and gene transcription, both their basic biology and their roles in disease development have been the subject of intense study. Because multiple post-translational modifications (PTMs) along the entire protein sequence are potential regulators of histones, a top-down approach, where intact proteins are analyzed, is ultimately required for complete characterization of proteoforms. However, significant challenges remain for top-down histone analysis primarily because of deficiencies in separation/resolving power and effective identification algorithms. Here we used state-of-the-art mass spectrometry and a bioinformatics workflow for targeted data analysis and visualization. The workflow uses ProMex for intact mass deconvolution, MSPathFinder as a search engine, and LcMsSpectator as a data visualization tool. When complemented with the open-modification tool TopPIC, this workflow enabled identification of novel histone PTMs including tyrosine bromination on histone H4 and H2A, H3 glutathionylation, and mapping of conventional PTMs along the entire protein for many histone subunits.

Extraction and Analysis of Inter-area Oscillation Using Improved Multi-signal Matrix Pencil Algorithm Based on Data Reduction in Power System

Abstract In this paper, a robust online approach based on wavelet transform and matrix pencil (WTMP) is proposed to extract the dominant oscillation mode and parameters (frequency, damping, and mode shape) of a power system from wide-area measurements. For accurate and robust extraction of parameters, WTMP is verified as an effective identification algorithm for output-only modal analysis. First, singular value decomposition (SVD) is used to reduce the covariance signals obtained by natural excitation technique. Second, the orders and range of the corresponding frequency are determined by SVD from positive power spectrum matrix. Finally, the modal parameters are extracted from each mode of reduced signals using the matrix pencil algorithm in different frequency ranges. Compared with the original algorithm, the advantage of the proposed method is that it reduces computation data size and can extract mode shape. The effectiveness of the scheme, which is used for accurate extraction of the dominant oscillation mode and its parameters, is thoroughly studied and verified using the response signal data generated from 4-generator 2-area and 16-generator 5-area test systems.

A Smart phone Identification Method Based on Gesture

In order to promote the practicality of smart phone identification based on gesture, we introduce weighted morphological characteristics and early termination of authentication into dynamic time warping (DTW) algorithm, which based on characteristic moving data captured by in-built accelerometer and gyroscopes, and put forward an effective identification algorithm (ME-DTW) for smartphone. The algorithm controls the contribution made by the difference between the latitudes to the total Euclidean distance by smartphone morphological characteristics. It also introduces restricted areas touch trigger gesture acquisition scheme and authentication gesture length selection scheme based on normal distribution, which effectively improve the identification accuracy and efficiency. Experimental results show that: when others imitate gestures attack false acceptance rate tends to 0%, the personal identification false rejection rate remained at 3.29%, which can meet most practical security needs

Parameter estimation in systems with binary-valued observations and structural uncertainties

This paper studies identification of linear systems with binary-valued observations generated via fixed thresholds. In addition to stochastic measurement noises, the systems are also subject to structural uncertainties, including deterministic unmodelled dynamics, nonlinear model mismatch, and sensor observation bias. Since binary-valued observations can supply only limited information on the signals, truncated empirical measures are introduced to extract further information for system identification. An effective identification algorithm is constructed based on the proposed empirical measures. Optimal identification errors, time complexity, optimal input design, and impact of disturbances, unmodelled dynamics, observation bias, and nonlinear model mismatch are thoroughly investigated in a stochastic information framework. Asymptotic upper and lower bounds are established on identification errors. Numerical experiments are presented to demonstrate the effectiveness of the algorithms and the main results.

AN AUTOMATIC ALGORITHM FOR IDENTIFICATION AND STRAIGHTENING IMAGES OF CURVED HUMAN CHROMOSOMES

Karyotyping is a standard method for presenting the complete set of the pictures of human chromosomes in a table-like format. It is usually used by a cytogenetic expert to predict the common genetic abnormalities. Producing a Karyotype from microscopic images of human chromosomes is a tedious and time-consuming task, so an automatic Karyotyping system would help the cytogenetic expert in his/her routine work. Automatic Karyotyping algorithms usually suffer the non-rigid nature of the chromosomes, which makes them to have unpredictable shapes and sizes in the images. One such problem that usually needs the operator's interaction is the existence of curved chromosomes within the images. In this paper, an effective algorithm for identification and straightening of curved human chromosomes is presented. This will extend the domain of application of the most of the previously reported algorithms to the curved chromosomes. The proposed algorithm is applied to single chromosomes that are initially modified by means of a Median filter. The medial axis (MA) of the filtered image is then extracted using a thinning procedure, which is carried out on the binary version of the image. By comparing the Euclidean distance of the endpoints and the length of the MA, a curved chromosome is identified. For chromosome straightening, the initially extracted medial axis is then modified by extending it in both ends considering the slope of the MA. Next, the original input image is intensity sampled over many closely located perpendicular lines to the MA along the chromosome which are then mapped into a matrix (as rows) producing a vertically oriented straight chromosome. For evaluation, the algorithm is applied to 54 selected highly curved chromosomes obtained at the pro-metaphase stage, which were provided by the Cytogenetic Laboratory of Cancer Institute, Imam Hospital, Tehran, Iran. The density profile and the centromeric index of the chromosomes which are among the most important and commonly used features for chromosome identification are calculated by the expert both before and after the straightening procedure. The mean squared error and the variance of the difference between the two are then obtained and compared. The results show a good agreement between the two, hence the effectiveness of the proposed method. The proposed algorithm therefore extends the domain of application of the previously reported algorithms to the highly curved chromosomes.

Combined rpo B duplex PCR and hsp65 PCR restriction fragment length polymorphism with capillary electrophoresis as an effective algorithm for identification of Mycobacterial species from clinical isolates

BackgroundMycobacteria can be quickly and simply identified by PCR restriction-enzyme analysis (PRA), but misidentification can occur because of similarities in band sizes that are critical for discriminating among species. Capillary electrophoresis can provide computer-aided band discrimination. The aim of this research was to develop an algorithm for identifying mycobacteria by combined rpoB duplex PRA (DPRA) and hsp65 PRA with capillary electrophoresis.ResultsThree hundred and seventy-six acid-fast bacillus smear-positive BACTEC cultures, including 200 Mycobacterium tuberculosis complexes (MTC) and 176 non-tuberculous mycobacteria (NTM) were analyzed. With combined hsp65 and rpoB DPRA, the accuracy rate was 100% (200 isolates) for the MTC and 91.4% (161 isolates) for the NTM. Among the discordant results (8.6%) for the NTM, one isolate of Mycobacterial species and an isolate of M. flavescens were found as new sub-types in hsp65 PRA.ConclusionsThis effective and novel identification algorithm using combined rpoB DPRA and hsp65 PRA with capillary electrophoresis can rapidly identify mycobacteria and find new sub-types in hsp65 PRA. In addition, it is complementary to 16 S rDNA sequencing.

Self-tuning PI-PID regulators for stable systems with varying delay

A new self-tuning PI-PID regulator is presented, based on a fixed-structure simplified model of the process. To cope with the majority of real-size applications, the simplified model is assumed to incorporate a variable or unknown time delay. Therefore, the core of the PI-PID regulator is an effective identification algorithm suitable for the on-line estimation of the unknown time delay in sampled systems. In the development, particular emphasis is given to all those aspects which make an algorithm really applicable in an industrial environment, such as start-up and initial tuning phase, choice of the sampling period, ease of the control synthesis criterion. Simulation experiments demonstrate the potential of the new regulator.

Effective identification algorithms for adaptive control

AbstractMinimum variance adaptive control schemes are considered. By means of the concept of excitation subspace, the notion of effective identification algorithm is introduced. It is shown that, if the system to be controlled is noise‐free and minimum phase, the tracking error tends to zero provided that the identification is effective. Finally, the effectiveness of the most popular recursive identification techniques (recursive least squares, stochastic gradient, projection algorithm) is discussed.