Modern Optimization Techniques for PID Parameters of Electrohydraulic Servo Control System

Electrohydraulic servo system has been used in industry in a wide number of applications. Its dynamics are highly nonlinear and also have large extent of model uncertainties and external disturbances. In order to in-crease the reliability, controllability and utilizing the superior speed of response achievable from electrohydraulic systems, further research is required to develop a control software has the ability of overcoming the problems of system nonlinearities. In This paper, a Proportional Integral Derivative (PID) controller is designed and attached to electrohydraulic servo actuator system to control its stability. The PID parameters are optimized by using four techniques: Particle Swarm Optimization (PSO), Bacteria Foraging Algorithm (BFA), Genetic Algorithm (GA), and Ant colony optimization (ACO). The simulation results show that the steady-state error of system is eliminated; the rapidity is enhanced by PSO applied on Proportional Integral Derivative (PPID), Bacteria Foraging Algorithm applied on Proportional Integral Derivative (BPID), GA applied on Proportional Integral Derivative (GPID), and ACO Algorithm applied on Proportional Integral Derivative (ACO-PID) controllers when the system parameter variation was happened, and has good performances using in real applications. A comparative study between used modern optimization techniques are described in the paper and the tradeoff between them

Speed Control of DC Motor Using Artificial Neural Network

This paper uses Artificial Neural Networks (ANNs) in estimating speed and controlling it for a separately excited DC motor which is one of the most important modern techniques that using in control applications and to improve efficiency speed control of separately excited DC motor (SEDM). The rotor speed of the DC motor can be made to follow an arbitrarily selected trajectory. The purpose is to achieve accurate trajectory control of the speed, especially when the motor and load parameters are unknown. Such a neural control scheme consists of two parts. One is the neural identifier which is used to estimate the motor speed. The other is the neural controller which is used to generate a control signal for a converter. These two neural networks are trained by Levenberg-Marquardt back-propagation algorithm. In this paper, the intelligent model is developed to speed control of SEDM which operated at two stages:-the first, NARMA-L2 controller used to control the speed under different external loads conditions. The second, the controller is performance at different reference speed. Simulation results indicates to the advantages, effectiveness, good performance of the artificial neural network controller which is illustrated through the comparison obtain by the system when using conventional controller (Proportional-Integral (PI)). So the results show ANN techniques provide accurate control and ideal performance at real tim

Pre Processing Techniques for Arabic Documents Clustering

Clustering of text documents is an important technique for documents retrieval. It aims to organize documents into meaningful groups or clusters. Preprocessing text plays a main role in enhancing clustering process of Arabic documents. This research examines and compares text preprocessing techniques in Arabic document clustering. It also studies effectiveness of text preprocessing techniques: term pruning, term weighting using (TF-IDF), morphological analysis techniques using (root-based stemming, light stemming, and raw text), and normalization. Experimental work examined the effect of clustering algorithms using a most widely used partitional algorithm, K-means, compared with other clustering partitional algorithm, Expectation Maximization (EM) algorithm. Comparison between the effect of both Euclidean Distance and Manhattan similarity measurement function was attempted in order to produce best results in document clustering. Results were investigated by measuring evaluation of clustered documents in many cases of preprocessing techniques. Experimental results show that evaluation of document clustering can be enhanced by implementing term weighting (TF-IDF) and term pruning with small value for minimum term frequency. In morphological analysis, light stemming, is found more appropriate than root-based stemming and raw text. Normalization, also improved clustering process of Arabic documents, and evaluation is enhanced

Face recognition using curvelet transform

This paper presents a new method for the problem of human face recognition from still images. This is based on a multiresolution analysis tool called Digital Curvelet Transform. Curvelet transform has better directional and edge representation abilities than wavelets. Due to these attractive attributes of curvelets, we introduce this idea for feature extraction by applying the curvelet transform of face images twice. The curvelet coefficients create a representative feature set for classification. These coefficients set are then used to train gradient descent backpropagation neural network (NN). A comparative study with wavelet-based, curvelet-based, and traditional Principal Component Analysis (PCA) techniques is also presented. High accuracy rate of 97 achieved by the proposed method for two well-known databases indicates the potential of this curvelet based curvelet feature extraction method

Optimize Wash Time of Washing Machine Using Fuzzy Logic

Washing machines are a common feature today in our household. The most important utility a customer can derive from a washing machine is that he saves the effort he/she had to put in brushing, agitating and washing the cloth. Most of the people wouldn’t have noticed (but can reason out very well (that different type of cloth need different amount of washing time which depends directly on the type of dirt, amount of dirt, cloth quality etc. The washing machines that are used today (the one not using fuzzy logic control) serves all the purpose of washing, but which cloth needs what amount of agitation time is a business which has not been dealt with properly. In most of the cases either the user is compelled to give all the cloth same agitation or is provided with a restricted amount of control. The thing is that the washing machines used are not as automatic as they should be and can be

Poor Quality Fingerprint Recognition Based on Wave Atom Transform

Fingerprint is considered the most practical biometrics due to some specific features which make them widely accepted. Reliable feature extraction from poor quality fingerprint images is still the most challenging problem in fingerprint recognition system. Extracting features from poor fingerprint images is not an easy task. Recently, Multi-resolution transforms techniques have been widely used as a feature extractor in the field of biometric recognition. In this paper we develop a complete and an efficient fingerprint recognition system that can deal with poor quality fingerprint images. Identification of poor quality fingerprint images needs reliable preprocessing stage, in which an image alignment, segmentation, and enhancement processes are performed. We improve a popular enhancement technique by replacing the segmentation algorithm with another new one. We use Waveatom transforms in extracting distinctive features from the enhanced fingerprint images. The selected features are matched throw K-Nearest neighbor classifier techniques. We test our methodology in 114 subjects selected from a very challenges database; CASIA; and we achieve a high recognition rate of about 99.5%

BFO vs. BSO for video object tracking using particle filter (PF)

In this paper, we introduced a new algorithm for Video tracking, which is the process of locating a moving object (or multiple objects) over time using a camera. A new particle filter based on bacteria foraging optimization (PF-BFO) is introduced in field of video object tracking. This paper reviews particle filter and using it for tracking. Particle Swarm Optimization (PSO) is also described. Moreover, using the combination of PSO with PF (PF-PSO) in video object tracking is reviewed. Bacterial Foraging Optimization (BFO) is a novel heuristic algorithm inspired from forging behavior of E. coli. After analysis of optimization mechanism, a series of measures are taken to improve the classic BFO by using Particle filter. The PSO is a meta-heuristic which is also inspired from insects' life as ACO. Even both methods use a population of entities. The comparison between PF-BFO and PF-PSO for video object tracking is presented in this work. The results show that PF is strong tool in tracking field. On the other hand, PF-BFO method presents outstanding performance versus PF-PSO

Spoken Arabic News Classification Based on Speech Features

One of the most important consequences of what is known as the "Internet era" is the widespread of varied electronic data. This deployment urgently requires an automated system to classify these data to facilitate search and access to the topic in question. This system is commonly used in written texts. Because of the huge increase of spoken files nowadays, there is an acute need for building an automatic system to classify spoken files based on topics. This system has been discussed in the previous researches applied to spoken English texts, but it rarely takes into consideration spoken Arabic texts because Arabic language is challenging and its dataset is rare and not suitable for topic classification. To deal with this challenge, a new dataset is established depending on converting the common written text (ALJ-NEWS) which is widely used in researches in classifying written texts. Then, keywords extraction method is implemented in order to extract the keywords representing each class depending on using DTW. Finally, topic identification, based on (MFCC, PLP-RASTA) as speech features and (DTW, HMM) as identifiers, is created using a technique that is different from the traditional way, using ASR to extract the transcriptions. Regarding the evaluation of the system, F1-measure, precision and recall are used as evaluation metrics. The proposed system shows positive results in the topic classification field. The F1-measure for topic identification system using DTW classifier records 90.26% and 91.36% using HMM classifier in the average. In addition, the system achieves 89.65% of keywords identification accuracy

Heart diseases diagnosis using heart sounds

Heart sound is one of the oldest means for assessing the function of heart valves. It helps, together with echocardiograms and electrocardiographs, to give a clear and proper diagnosis of several diseases. Artificial neural networks are used to classify several valve-related heart disorders. A library of heart sound files, recorded via the traditional stethoscope, are used to extract relevant features using several signal processing tools, e.g., discrete wavelet transform (DWT), fast Fourier transform (FFT) and linear predictive coding (LPC). The achieved recognition rates were around 95.7%