Resul Coteli

A new automatic target recognition system based on wavelet extreme learning machine

In this paper, an automatic system is presented for target recognition using target echo signals of High Resolution Range (HRR) radars. This paper especially deals with combination of the feature extraction and classification from measured real target echo signal waveforms by using X-band pulse radar. The past studies in the field of radar target recognition have shown that the learning speed of feedforward neural networks is in general much slower than required and it has been a major disadvantage. There are two key reasons forth is status of feedforward neural networks: (1) the slow gradient-based learning algorithms are extensively used to train neural networks, and (2) all the parameters of the networks are tuned iteratively by using such learning algorithms (Feng, Huang, Lin, & Gay, 2009; Huang & Siew, 2004, 2005; Huang & Chen, 2007, 2008; Huang, Chen, & Siew, 2006; Huang, Ding, & Zhou, 2010; Huang, Zhu, & Siew, 2004; Huang, Liang, Rong, Saratchandran, & Sundararajan, 2005; Huang, Zhou, Ding, & Zhang, 2012; Huang, Li, Chen, & Siew, 2008; Huang, Wang, & Lan, 2011; Huang et al., 2006; Huang, Zhu, & Siew, 2006a, 2006b; Lan, Soh, & Huang, 2009; Li, Huang, Saratchandran, & Sundararajan, 2005; Liang, Huang, Saratchandran, & Sundararajan, 2006; Liang, Saratchandran, Huang, & Sundararajan, 2006; Rong, Huang, Saratchandran, & Sundararajan, 2009; Wang & Huang, 2005; Wang, Cao, & Yuan, 2011; Yeu, Lim, Huang, Agarwal, & Ong, 2006; Zhang, Huang, Sundararajan, & Saratchandran, 2007; Zhu, Qin, Suganthan, & Huang, 2005). To resolve these disadvantages of feedforward neural networks for automatic target recognition area in this paper suggested a new learning algorithm called extreme learning machine (ELM) for single-hidden layer feedforward neural networks (SLFNs) (Feng, Huang, Lin, & Gay, 2009; Huang & Siew, 2004, 2005; Huang & Chen, 2007, 2008; Huang, Chen, & Siew, 2006; Huang, Ding, & Zhou, 2010; Huang, Zhu, & Siew, 2004; Huang, Liang, Rong, Saratchandran, & Sundararajan, 2005; Huang, Zhou, Ding, & Zhang, 2012; Huang, Li, Chen, & Siew, 2008; Huang, Wang, & Lan, 2011; Huang et al., 2006; Huang, Zhu, & Siew, 2006a, 2006b; Lan, Soh, & Huang, 2009; Li, Huang, Saratchandran, & Sundararajan, 2005; Liang, Huang, Saratchandran, & Sundararajan, 2006; Liang, Saratchandran, Huang, & Sundararajan, 2006; Rong, Huang, Saratchandran, & Sundararajan, 2009; Wang & Huang, 2005; Wang, Cao, & Yuan, 2011; Yeu, Lim, Huang, Agarwal, & Ong, 2006; Zhang, Huang, Sundararajan, & Saratchandran, 2007; Zhu, Qin, Suganthan, & Huang, 2005) which randomly choose hidden nodes and analytically determines the output weights of SLFNs. In theory, this algorithm tends to provide good generalization performance at extremely fast learning speed. Moreover, the Discrete Wavelet Transform (DWT) and wavelet entropy is used for adaptive feature extraction in the time-frequency domain in feature extraction stage to strengthen the premium features of the ELM in this study. The correct recognition performance of this new system is compared with feedforward neural networks. The experimental results show that the new algorithm can produce good generalization performance in most cases and can learn thousands of times faster than conventional popular learning algorithms for feedforward neural networks.

Modeling of Drying Process of Bittim Nuts (pistacia terebinthus) in a Fixed Bed Dryer System by Using Extreme Learning Machine

Abstract Artificial neural networks (ANNs) have been widely used in modeling of various systems. Training of ANNs is commonly performed by backpropagation based on a gradient-based learning rule. However, it is well-known that such learning rule has several shortcomings such as slow convergence and training failures. This paper proposes a modeling technique based on Extreme Learning Machine (ELM) eliminating disadvantages of backpropagation based on a gradient-based learning rule for the drying of bittim (pistacia terebinthus). The samples for ELM based model are obtained by experimental studies. In experimental studies, the sample mass loss rate as a function time was investigated in different air velocities (0.5 and 1 m/s) and air temperatures (40, 60 and 80°C) in a designed dryer system. The obtained samples from experiments are used for training and testing of ELM. Further, some parameters of ELM such as type of activation function and the number of hidden neurons are set to obtain the best possible modelling results. The obtained prediction results show that ELM algorithm with tangent sigmoid activation function and 20 hidden neurons is appeared to be most optimal topology since maximum R2 and minimum rms (0.0500) and cov (0.2256) values are obtained. Thus, it is concluded that ELM can be used as an effective modelling tool in the drying of bittim (pistacia terebinthus) in fixed bed dryer system.

Three-level Cascaded Inverter Based D-STATCOM Using Decoupled Indirect Current Control

Abstract In this paper, experimental setup of three-level cascaded inverter based 380V/±25kVAR D-STATCOM using decoupled indirect current control method (DICC) is realized. AC and DC side of D- STATCOM is modelled in dq-axis on account of D-STATCOM’s controlling. DICC is used for control of D-STATCOM’s dq-axis currents independently. Gate pulses for inverter are generated with multilevel sinusoidal pulse width modulation (SPWM) technique. In this study, controller card used for signal processing is dSPACEs DS1103. The control algorithm is prepared by the help of MATLAB/ Simulink® software. This algorithm is converted to C language by using Matlab/Real-Time Workshop and downloaded to DS1103’s program memory by dSPACE/Real-Time Interface. Implemented experimental setup is tested by changing reference reactive current (iqref) +20A to −20A and obtained results from this test are given.

A new method for object detection from binary images

There could be one or more objects in an image. Object detection algorithms are needed to detect these objects. Many methods have been proposed for the detection of objects. These methods are based on various feature extraction methods. In this study, a new algorithm is proposed for the detection of objects. The proposed method is object detection model based on thresholding. In this model, image is firstly converted into binary form and coordinates points of the object on the image are determined. The detected objects are determined by using these coordinate points of the object on the image are determined and the desired object is extracted.

Virtual Laboratory for Power Electronic Based Reactive Power Compensators

The practical application is important in engineering education. However, in some cases, such as fund deficiency, practical applications may not be possible. In addition, this case restricts closely to pursue the novel technologies. Depending on developments in power electronics and microprocessor fields, use of Flexible Alternating Current Transmission Systems (FACTS) devices have been becoming more common. Practical applications on these devices are insufficient in undergraduate education. This study presents a virtual laboratory application for power electronics based FACTS devices. The virtual laboratory for education of advanced compensation method is prepared by using MATLAB GUI. The all parameters belonging to the related circuit can be accessed via the prepared virtual laboratory. Over a visual interface, current, voltage and power values can be observed. Theoretical information about the related subject is also included in the prepared virtual laboratory. All possible applications can be done even if users do not know MATLAB Programming Language, GUI, any formula or command from Simulink. User can repeat the application as much as they want and observe results obtained from different parameter values.