Showing 12 results for Identification
J. Poshtan, H. Mojallali,
Volume 1, Issue 1 (1-2005)
Abstract
We give a general overview of the state-of-the-art in subspace system
identification methods. We have restricted ourselves to the most important ideas and
developments since the methods appeared in the late eighties. First, the basis of linear
subspace identification are summarized. Different algorithms one finds in literature (Such
as N4SID, MOESP, CVA) are discussed and put into a unifying framework. Further, a
comparison between subspace identification and prediction error methods is made on the
basis of computational complexity and precision of methods by applying them to a glass
tube manufacturing process.
A. Ebrahimzadeh, S. A. Seyedin,
Volume 1, Issue 4 (10-2005)
Abstract
Automatic signal type identification (ASTI) is an important topic for both the
civilian and military domains. Most of the proposed identifiers can only recognize a few
types of digital signal and usually need high levels of SNRs. This paper presents a new high
efficient technique that includes a variety of digital signal types. In this technique, a
combination of higher order moments and higher order cumulants (up to eighth) are
proposed as the effective features. A hierarchical support vector machine based structure is
proposed as the classifier. In order to improve the performance of identifier, a genetic
algorithm is used for parameters selection of the classifier. Simulation results show that the
proposed technique is able to identify the different types of digital signal (e.g. QAM128,
ASK8, and V29) with high accuracy even at low SNRs.
D. Arab-Khaburi, F. Tootoonchian, Z. Nasiri-Gheidari,
Volume 3, Issue 1 (1-2007)
Abstract
Because of temperature independence, high resolution and noiseless outputs,
brushless resolvers are widely used in high precision control systems. In this paper, at first
dynamic performance characteristics of brushless resolver, considering parameters
identification are presented. Then a mathematical model based on d-q axis theory is given.
This model can be used for studying the dynamic behavior of the resolver and steady state
model is obtained by using dynamic model. The main object of this paper is to present an
approach to identify electrical and mechanical parameters of a brushless resolver based on
DC charge excitation and weight, pulley and belt method, respectively. Finally, the model
of resolver based on the obtained parameters is simulated. Experimental results approve the
validity of proposed method.
M. Hariri, S. B. Shokouhi, N. Mozayani,
Volume 4, Issue 3 (10-2008)
Abstract
Dealing with uncertainty is one of the most critical problems in complicated
pattern recognition subjects. In this paper, we modify the structure of a useful Unsupervised
Fuzzy Neural Network (UFNN) of Kwan and Cai, and compose a new FNN with 6 types of
fuzzy neurons and its associated self organizing supervised learning algorithm. This
improved five-layer feed forward Supervised Fuzzy Neural Network (SFNN) is used for
classification and identification of shifted and distorted training patterns. It is generally
useful for those flexible patterns which are not certainly identifiable upon their features. To
show the identification capability of our proposed network, we used fingerprint, as the most
flexible and varied pattern. After feature extraction of different shapes of fingerprints, the
pattern of these features, “feature-map”, is applied to the network. The network first
fuzzifies the pattern and then computes its similarities to all of the learned pattern classes.
The network eventually selects the learned pattern of highest similarity and returns its
specific class as a non fuzzy output. To test our FNN, we applied the standard (NIST
database) and our databases (with 176×224 dimensions). The feature-maps of these
fingerprints contain two types of minutiae and three types of singular points, each of them
is represented by 22×28 pixels, which is less than real size and suitable for real time
applications. The feature maps are applied to the FNN as training patterns. Upon its setting
parameters, the network discriminates 3 to 7 subclasses for each main classes assigned to
one of the subjects.
Saba Sedghizadeh , Caro Lucas , Hassan Ghafoori Fard ,
Volume 5, Issue 2 (6-2009)
Abstract
An adaptive online flux-linkage estimation method for the sensorless control of switched reluctance motor (SRM) drive is presented in this paper. Sensorless operation is achieved through a binary observer based algorithm. In order to avoid using the look up tables of motor characteristics, which makes the system, depends on motor parameters, an adaptive identification algorithm is used to estimate of the nonlinear flux-linkage parameters. This method makes position and speed estimation more accurate and robust towards any model uncertainty, also it is suitable replacement for a priori knowledge of motor characteristics.
M. Barati, A. R. Khoogar, M. Nasirian,
Volume 7, Issue 4 (12-2011)
Abstract
Abstract: Using robot manipulators for high accuracy applications require precise value of the kinematics parameters. Since measurement of kinematics parameters are usually associated with errors and accurate measurement of them is an expensive task, automatic calibration of robot link parameters makes the task of kinematics parameters determination much easier. In this paper a simple and easy to use algorithm is introduced for correction and calibration of robot kinematics parameters. Actually at several end-effecter positions, the joint variables are measured simultaneously. This information is then used in two different algorithms least square (LS) and Genetic algorithm (GA) for automatic calibration and correction of the kinematics parameters. This process was also tested experimentally via a three degree of freedom manipulator which is actually used as a coordinate measuring machine (CMM). The experimental Results prove that the Genetic algorithms are better for both parameter identification and calibration of link parameters.
M. Maboodi, M. H. Ashtari, M. Aliyari Shoorehdeli,
Volume 8, Issue 3 (9-2012)
Abstract
This paper addresses the experimental identification of a servo actuator which is used in many
industrial applications. Because the system consisted of electrical and mechanical components, the
behavior of the system was nonlinear. In addition, the under load behavior of this servo was different. The
load torque was considered as the input and a two input-one output model was presented for this servo
actuator. Special focus was given in order to present a simple model for this servo actuator. The
comparison between simulation and experimental results showed the effectiveness of the propose model.
The model can be applied as a reference for characterizing different designs and future control strategies.
Z. Gallehdari, M. Dehghani, S. K. Nikravesh,
Volume 10, Issue 2 (6-2014)
Abstract
The purpose of this paper is to present a new approach based on the Least Squares Error method for estimating the unknown parameters of the nonlinear 3rd order synchronous generator model. The proposed method uses the mathematical relationships between the machine parameters and on-line input/output measurements to estimate the parameters of the nonlinear state space model. The field voltage is considered as the input and the rotor angle and the active power are considered as the generator outputs. In fact, the third order nonlinear state space model is converted to only two linear regression equations. Then, easy-implemented regression equations are used to estimate the unknown parameters of the nonlinear model.
The suggested approach is evaluated for a sample synchronous machine model. Estimated parameters are tested for different inputs at different operating conditions. The effect of noise is also considered in this study. Simulation results show that the proposed approach provides good accuracy for parameter estimation.
S. M. Mousavi Gazafroodi, A. Dashti,
Volume 10, Issue 4 (12-2014)
Abstract
In this paper, a novel stator current based Model Reference Adaptive System (MRAS) estimator for speed estimation in the speed-sensorless vector controlled induction motor drives is presented. In the proposed MRAS estimator, measured stator current of the induction motor is considered as a reference model. The estimated stator current is produced in an adjustable model to compare with the measured stator current, where rotor flux identification is needed for stator current estimation. In the available stator current based MRAS estimator, rotor flux is estimated by the use of measured stator current, where the adjustable model and reference model depend on each other since measured stator current is employed in both of them. To improve the performance of the MRAS speed estimator, both the stator current and rotor flux are estimated in the adjustable model by using the state space equations of the induction motor, adjusted with the rotor speed calculated by an adaptation mechanism. The stability of the proposed MRAS estimator is studied through a small signal analysis. Senorless induction motor drive along with the proposed MRAS speed estimator is verified through computer simulations. In addition, performance of the proposed MRAS is compared with the available stator current based MRAS speed estimator
S. Prasad Tiwari,
Volume 19, Issue 3 (9-2023)
Abstract
In spite of the numerous benefits over the traditional power distribution system, protection of the microgrid is a challenging and complex task. The varying fault resistances due to dissimilar grounding conditions can affect the performance of the protection scheme. Under such conditions, the magnitude of the fault current can vary from lower to higher level. In addition to the above, the dissimilar magnitude of fault current during grid connected and islanded mode demands a protection scheme that can easily discriminate the mode of operation. The magnitude of fault current in grid-connected and islanded modes needs a robust protection scheme. In this regard, an ensemble of subspace kNN based robust protection scheme has been proposed to detect the faulty conditions of the microgrid. The tasks of the mode detection, fault detection/classification as well as faulty line identification has been carried out in the proposed work. In the proposed protection scheme, discrete wavelet transform (DWT) has been used for processing of the data. After recording the voltage and current signals at bus-1, the protection scheme has been validated. The validation of the protection scheme in Section 6 reveals that the protection scheme is efficiently working.
Ramin Safaeian, Mahmoud Tabandeh ,
Volume 20, Issue 2 (6-2024)
Abstract
Directed Acyclic Graphs stand as one of the prevailing approaches for representing causal relationships within a set of variables. With observational or interventional data, certain undirected edges within a causal DAG can be oriented. Performing intervention can be done in two different settings, passive and active. Here, we prove that an optimal intervention set can be obtained based on the minimum vertex cover of a graph. We propose an algorithm that efficiently identifies such an optimal intervention set for chordal graphs within polynomial time. Performing intervention on this optimal set recovers all the undirected edges in graph G, regardless of the underlying ground truth DAG. Furthermore, we present an algorithm for evaluating the performance of passive algorithms. This evaluation provides insights into how many intervention steps of a specific algorithm are required to recover all edges in the causal graph for any possible underlying ground truth in the equivalence class. Experimental findings underscore that the number of nodes in the optimal intervention set increases with growing the number of nodes in a graph, where the edge density is fixed, and also increases with the rising edge density in a graph with a fixed number of nodes.
Edy Victor Haryanto S, Aimi Salihah Abdul Nasir, Mohd Yusoff Mashor, Bob Subhan Riza, Zeehaida Mohamed,
Volume 21, Issue 2 (6-2025)
Abstract
Malaria is a parasitic disease that causes significant morbidity and mortality worldwide. Early diagnosis and treatment are crucial for preventing complications and improving patient outcomes. Microscopic examination of blood smears remains the gold standard for malaria diagnosis, but it is time-consuming and requires skilled technicians. Deep learning has emerged as a promising tool for automated image analysis, including malaria diagnosis. In this study, we propose a novel approach for identifying malaria parasites in microscopic images using the GoogLeNet. Our method includes enhancement with the AGCS method, color transformation with grayscale, adaptive thresholding for segmentation, extraction, and GoogLeNet-based classification. We evaluated our method on a dataset of malaria blood smear images and achieved an accuracy of 95%, demonstrating the potential of GoogLeNet for automated malaria diagnosis.
