International Journal of Engineering
Year:2014 | Volume:27 | Issue:1 (TRANSACTIONS A: BASICS)|Papers Count:18

In this paper, a new algorithm which is the result of combination of cellular learning automata (CLA) and shuffled frog leap algorithm (SFLA) is proposed for optimization of functions in continuous, static environments. In the frog leaping algorithm, every frog represents a feasible solution within the problem space. In the proposed algorithm, each memeplex of frogs is placed in a cell of CLA. Learning automata in each cell acts as the brain of memeplex and will determine the strategy of motion and search. The proposed algorithm along with the standard SFLA and two global and local versions of particle swarm optimization algorithm have been tested in 30-dimensional space on five standard merit functions. Experimental results show that the proposed algorithm has a performance of the introduced algorithm is due to the control of search behavior of frogs during the optimization process.

Keyword Spotting is a well-known method in document image retrieval which is based on query word image. In this paper, a document image retrieval system based on keyword spotting and relevance feedback is presented. Relevance feedback as an interactive method is used in this paper to improve the performance of Document Image Retrieval System (DIRS). In the proposed method, we compare several strategies of positive and negative feedbacks which include “Only Positive Feedback”, “Only Negative Feedback” and “Positive and Negative Feedback”. Experiments show that using relevance feedback in DIR outperforms common DIR.

The main issue in any image zooming techniques is to preserve the structure of the zoomed image. The zoomed image may suffer from the discontinuities in the soft regions and edges; it may contain artifacts, such as image blurring and blocky, and staircase effects. This paper presents a novel image zooming technique using Partial Differential Equations (PDEs). It combines a non-linear Fourth-order PDE method with the Locally Adaptive Zooming (LAZ) algorithm. The proposed method uses high-resolution image obtained from LAZ algorithm to construct zoomed image by Fourth-order PDE. This proposed method preserves edges and minimizes blurring and staircase effects in the zoomed image. In order to evaluate image quality obtained from the proposed method, this paper focuses on both subjective and objective assessments. The results of these measures on a variety of images show that the proposed method is superior over the other image zooming methods.

This research puts forward the rough finite state automata which have been represented by two variants of Binary Decision Diagram (BDD) called ROBDD and ZBDD, for networked system diagnosis. Using the suggested data structures can help us overcome the combinatorial explosion which usually occurs in system diagnosis. In implementations and analysis of our experimental results, we used CUDD-Colorado University Decision Diagram package. A mathematical proof for the claimed complexity is provided which shows that ZBDD representation has superiority in space and time complexity to ROBDD representation.v

A wireless ad hoc network typically refers to any set of wireless networks where all devices have equal status on the network and are free to associate with any other wireless ad hoc network devices in their range. Due to their nature, these networks commonly do not have external power supplies, and each node has a limited internal power source. In this paper, we put forward a new routing protocol that is light enough for energy constrained networks, and find routes on the basis of remaining power of each node. Besides, we have also taken some other factors into consideration. These factors include the ratio of remaining power of a node to the distance of the node from next receiving node and the ratio of the remaining power of current node to the average remaining power of the whole network. This protocol puts emphasis on routing in such a way that the network stays heterogeneous in terms of remaining power of nodes. This homogeneity brings on a longer network lifetime. In addition, the presented protocol does not impose a high computational load on the network. Hence, it is lightweight enough to be used in networks with low computational power and constrained power resources.

Routing is an important challenge in WSN due to the presence of hundreds or thousands of sensor nodes. Low Energy Adaptive Clustering Hierarchy (LEACH) is a hierarchical routing and data dissemination protocol. LEACH divides a network domain into several sub-domains that are called clusters. Non-uniformity of cluster distribution and cluster heads (CHs) selection without considering the positions of other sensors may reduce the quality of cluster selection. Sensor nodes send data packets over long distances. Imperialist Competitive Algorithm (ICA) is an optimization algorithm inspired by social phenomenon. It considers colonization process as a stage of socio-political evolution. We improve performance of the LEACH algorithm using imperialist approach and study its efficacy in terms of energy consumption, coverage and cluster uniformity and compare with those of the LEACH algorithm. Selection of suitable value for radio communication radius over the network lifetime is a trade-off between connectivity and sensors energy consumption. Empowering LEACH with ICA helps to find the best location of a CH in every cluster, conserve energy significantly, increase network lifetime, and maintain network connectivity. It can significantly reduce the number of active sensors going out of range over the lifetime of a network.

Alzheimer's disease is characterized by impaired glucose metabolism and demonstration of amyloid plaques. Individual positron emission tomography tracers may reveal specific signs of pathology that is not readily apparent on inspection of another one. Combination of multitracer positron emission tomography imaging yields promising results. In this paper, 57 Alzheimer's disease neuroimaging initiative subjects that had FDG and PiB-positron emission tomography neuroimaging scans at the same time were used for development of proposed multitracer classification method. The subject’s brain image was automatically parcellated into 48 pre-defined regions of interest. Then, 96 features were extracted for each subject. The principal features weere extracted using principal component analysis, then they were combined based on intersection strategy. Finally, a support vector machine was adopted to evaluate the classification accuracy. Combination of two tracers with positron emission tomography scan yielded a higher diagnostic accuracy in Alzheimer's disease compared to individual tracer and other combination methods.

According to importance and security of energy in recent years, too many research about renweable energy have been done. Among renewable energy sources, fuel cell has far attention, because the efficiency of fuel cells is always higher as compared with other distributed generation systems. Fuel cell has a lot of advantages such as: high efficiency (35%-60%), low to zero emissions, quiet operation, high reliability due to the limited number of moving parts and the ability to be placed at any site in a distribution system without geographic limitations. Management in supplying residential load with fuel cell power plant (FCPP) connected to grid is the aim of this paper. Economical fuel cell model includes Operational cost, startup cost and different tariffs on electricity during the day hour is discussed. In the present research, reformer is used to produce hydrogen for FCPP. Genetic algorithm (GA) is used to determine optimal operation of FCPP with six-minute change in load pattern. The results are discussed in conclusion.

In this paper, we present a new model for calculating the capacitance of MEMS sensor with corrugated diaphragm. In this work, the effect of residual stress is considered on deflection of diaphragm and capacitance of sensor. First, a new analytical analyzes have been carried out to derive mathematic expression for central deflection of corrugated diaphragm and its relationship with residual stress. Then, the capacitance and sensitivity of sensor using corrugated diaphragm with residual stress are calculated under bias voltage and pressure. The analytical results are compared with simulation using a Finite Element Method (FEM). The results show that the new analytical model is very close with simulation results.

Winding function method (WFM) provides a detailed and rather simple analytical modeling and simulation technique for analyzing performance of faulty squirrel-cage induction motors (SCIMs). Such analysis is mainly applicable for designing on-line fault diagnosis techniques. In this paper, WFM is extended to include variable degrees of magnetic saturation by applying an appropriate air gap function and novel techniques for estimating required saturation factor and angular position of the air gap flux density. The resultant saturable winding function method (SWFM) is properly applied to analyze SCIMs with broken rotor bars and to identify the saturation effect on the related fault indexes. Comparing simulation results to the corresponding experimental ones show higher accuracy of the SWFM. This means that more precise fault diagnosis techniques can be designed by using the proposed saturable modeling technique.

Recent developments in robotics automation have motivated researchers to improve the efficiency of interactive systems by making a natural man-machine interaction. Since speech is the most popular method of communication, recognizing human emotions from speech signal becomes a challenging research topic known as Speech Emotion Recognition (SER). In this study, we propose a Persian emotional speech corpus collected from emotional sentences of drama radio programs. Moreover, we propose a new automatic speech emotion recognition system which is used both for spectral and prosodic feature simultaneously. We compare the proposed database with the public and widely used Berlin database. The proposed SER system is developed for females and males separately. Then, irrelevant features are removed using Fisher Discriminant Ratio (FDR) filtering feature selection technique. The selected features are further reduced in dimensions using Linear Discriminant Analysis (LDA) embedding feature reduction scheme. Finally, the samples are classified by a LDA classifier. The overall recognition rate of 55.74% and 47.28% is achieved on proposed database for females and males, respectively. Also, the average recognition rate of 78.64% and 73.40% are obtained for Berlin database for females and males, respectively.

A new method is introduced here for root detection in minirhizotron images for root investigation. In this method, a hypothesis testing framework is defined first to separate roots from background and noise. Then, the correct roots are extracted by an entropy-based geometric level set decision function. Performance is evaluated on real captured images in two different scenarios. In the first scenario, images contain several roots while the second scenario belongs to no-root images, which increases the chance of false detections. The results demonstrate better capability of the proposed method in root detection compared to the present approaches in all the cases investigated. Furthermore, it can be shown that better detection of roots in the proposed algorithm not only does not lead to extracting more false particles, but also it decreases rate of false detections compared to the existing algorithms.

A permanent physical fault in communication lines usually leads to a failure. The feasibility of evolution of a self-organized communication is studied in this paper to mitigate this problem. In this case a communication protocol may emerge between blocks and also can adapt itself to environmental changes like physical faults and defects. In spite of the faults, blocks may continue to function, since a self-organized nature can provide self-healing capabilities. In the present paper, Evolvable Hardware is to create such a fault tolerant communication without any predefined protocol using a GA algorithm. Evolvable Hardware is a concept that aims the application of evolutionary algorithms to hardware design. The feasibility of this idea is studied in simu lation of two reconfigurable blocks that are intended to transmit video streams through their communication lines. Permanent physical faults are induced in the communication lines between Evolvable Hardware blocks. Although the results show the emergence of fault tolerant protocols among Evolvable Hardware blocks without human intervention, there are some limitations in functional and gate level evolution of the blocks. Thus, a new adaptive approach is presented in this paper to defeat some limitations like the stalling effect of GA in faulty conditions.

A discrete event system (DES) is a dynamic system that evolves in accordance with the abrupt occurrence, at possibly unknown irregular intervals, of physical events. Because of the special nature of these systems, different tools are currently used for their analysis, design and modeling. The main focus of this paper is the presentation of a new modeling approach of Discrete Event Systems. The proposed approach is based on hybrid model which combines Interval Constrained Petri Nets (ICPN) and Timed Automata. These tools allow us to evaluate, respectively, the quality variations and to manage the flow type disturbance. An example analysis illustrates our approach.

In this paper, a boost converter with a clamp circuit is proposed for high intensity discharge (HID) lamp application. The clamp circuit provides zero voltage turn on for both main and clamp switches. Compared to conventional boost converters, the proposed converter has the following advantages: (i) high voltage gain without suffering from extreme duty ratio, (ii) low stress on the switches and (iii) low switching losses. Simulation and experimental results show that the voltage stress on the switches are well within acceptable limits and prove the converter’s performance over a wide load range.

In this paper, the immune controller is used to control the paralleled DC-DC converters. A PID controller is first applied and its coefficient is optimized using an intelligent (PSO) algorithm. Immune controller is then added to PID controller and an immune PID controller is formed. Two methods have been suggested to determine non-linear behavior of immune controller. In the first method, an exponential function is suggested and its unknown coefficient is optimized using PSO algorithm. In the second method, fuzzy logic has been used. Performance of the proposed control methods in the presence of various disturbances is investigated over a sample paralleled DC-DC converter and the effectiveness of the applied immune controller is verified with the comparison of simulation results. The results show that the improvement of system performance with immune PID controller by two suppression function in comparison with the PID controller.

In the presented paper, a sensorless driver circuit is designed, constructed and tested to control two types of three-phase Switched Reluctance Motor (SRM). The presented control algorithm has three steps. In the first step, the SRM is started and controlled by an open-loop method. In the second step, a novel method is introduced which uses the optimized flux-current-rotor position relation in order to calculate the position of the rotor and then control the motor in low speed. In the third step, the current waveform of the excited phase of the motor is utilized in order to detect the rotor position and control the SRM in high speed. Because of using the presented novel method, a low cost and simple sensorless driver circuit has been designed. In the circuit, an inexpensive Atmega128 AVR microcontroller is used to implement the control algorithms and generate gate pulses of transistors. Asymmetric bridge converter with N-channel Mosfets is used in order to excite the winding of phases. The parameters of the SRMs which are required for designing the sensorless driver circuit are obtained by simulating and analyzing the motors by Finite Element Method (FEM). The constructed circuit is tested in laboratory by driving the selected SRMs in no-load condition and the operational results are obtained and compared to each other at the end.

The gimbal stabilization mechanism is used to provide the stability to an object mounted on the gimbal by isolating it from the base angular motion and vibration. The purpose of this paper is to present a model of control servo system for one axis gimbal mechanism using a cascade PID controller. The gimbal torque relationships are derived by taking into consideration the base angular motion. The conventional PID controller and three cascade controller structures are investigated. The servo control loop is built and modelled in MATLAB/Simulink using these controllers. The simulation results are compared and the servo system performance is analysed for each controller in terms of performance criteria. The comparison results prove that a further improved system performance is achieved using I-PD controller as compared to the system performance obtained when the other controllers are utilized. The paper’s value lies in designing the servo control system using a modified controller composed of two parallel I-PD controllers related with a switch depend ing on the base angular rate as a threshold. The results show that the modified system satisfies the desired servo system requirements.