Development of a semi-active car suspension control system using magneto-rheological damper model

In this paper, the development of a semi- active suspension control of quarter car model using fuzzy-based controller has been done. The quarter car model to be used here can be described as a nonlinear two degrees of freedom system which is subject to excitation from different road profile. The semi-active control is designed as the fuzzy control inferred by using two single input rule fuzzy modules, and the road model is used as the control force is released by actuating an electromagnetic shaker. To implement semi-active suspension system experimentally, the MR damper is used here as the adjustable damper. The MR damper is a control device that consists of a hydraulic cylinder filled with magnetically polarizable particles suspended in a liquid. MR dampers dissipate vibration by absorbing energy. Magnetorheological (MR) fluids dampers are very effective to control vibration, which use MR fluids to produce controllable damping force and provide both the reliability of passive systems and the facility of active control systems with small power supply. Due to their mechanical simplicity, high dynamic range, low power requirements, large force capacity, and robustness, offer an attractive means of vibration protection. The objectives of this are modeling of semi-active suspension system, developing controller and understanding the characteristics of the MR damper to provide effective damping for the purpose of suspension isolation or suppression car model. In this work pid, fuzzy logic and fuzzy-hybrid controller are used to control semi-active car suspension system

Application of intelligent technique for development of Colpitts oscillator

In this paper, new method of Colpitts oscillator designing through combination of Genetic Algorithm and Artificial Neural Network (ANN) has been suggested. The Thevenin's resistors for the common base Colpitts oscillator are optimized through application of GA and ANN. The developed common base Colpitts oscillator has shortest transient time response and stable Direct Current (DC) stability in the long term operation. Involvement of GA and ANN successfully optimize between transient time response and steady state response of common base oscillator. Application of these two artificial intelligent techniques assist faster selection of optimizes components values such as resistance values during circuit development rather than conventional method which used intuition techniques to develop the circuit

Evaluating the effectiveness of time-domain features for motor imagery movements using SVM

Motor imagery electroencephalogram signals are the only bio-signals that enable locked-in patients, who have lost control over every motor output, to communicate with and control their surroundings. Brain Machine Interface is collaboration between a human and machines, which translates brain waves to desired, understandable commands for a machine. Classification of motor imagery tasks for BMIs is the crucial part. Classification accuracy not only depends on how accurate and robust the classifier is; it is also about data. For well separated data, classifiers such as kernel SVM can handle classification and deliver acceptable results. If a feature provides large interclass difference for different classes, immunity to random noise and chaotic behavior of EEG signal is rationally conformed, which means the applied feature is suitable for classifying EEG signals. In this work, in order to have less computational complexity, time-domain algorithms are employed to motor imagery signals. Extracted features are: Mean Absolute Value, Maximum peak value, Simple Square Integral, Willison Amplitude, and Waveform Length. Support Vector Machine with polynomial kernel is applied for classification of four different classes of data. The obtained results show that these features have acceptable, distinct values for different these four motor imagery tasks. Maximum classification accuracy belongs to contribution of Willison amplitude as feature and SVM as classifier, with 95.1 percentages accuracy. Where, the lowest is the contribution of Waveform Length and SVM with 31.67 percentages classification accuracy

Evaluation of time-domain features for motor imagery movements using FCM and SVM

Brain–Machine Interface is a direct communication pathway between brain and an external electronic device. BMIs aim to translate brain activities into control commands. To design a system that translates brain waves and its activities to desired commands, motor imagery tasks classification is the core part. Classification accuracy not only depends on how capable the classifier is but also it is about the input data. Feature extraction is to highlight the properties of signal that make it distinct from the signal of the other mental tasks. Performance of BMIs directly depends on the effectiveness of the feature extraction and classification algorithms. If a feature provides large interclass difference for different classes, the applied classifier exhibits a better performance. In order to attain less computational complexity, five timedomain procedure, namely: Mean Absolute Value, Maximum peak value, Simple Square Integral, Willison Amplitude, and Waveform Length are used for feature extraction of EEG signals. Two classifiers are applied to assess the performance of each feature-subject. SVM with polynomial kernel is one of the applied nonlinear classifier and supervised FCM is the other one. The performance of each feature for input data are evaluated with both classifiers and classification accuracy is the considered common comparison parameter

Development of a microcontroller-based control system with a hardware-in-the-loop (HIL) method for control education using MATLAB/simulink/xPC Target

This paper discusses the development of a microcontroller-based control system with hardware-inthe-loop (HIL) for enhancing the teaching of control engineering. This system consists of both hardware and software. The software tools MATLAB/Simulink/xPC Target by MathWorks, Inc and a C++ compiler are used to simulate the physical system (plant) to be controlled, while the hardware include a microcontroller as a controller and interfacing circuits to allow communication between the simulated plant and the real controller. This proposed system is inexpensive and allows students to carry out extensive experimental investigations, as well as the design, implementation, performance evaluation and comparative studies of controllers. A case study of the controller design and implementation for an active suspension system is presented to illustrate the application of the proposed system

Effect of tuber skin on the thermal properties of whole tubers of potato and sweet potato

Temperature-dependent thermal coefficients of mathematical models of the postharvest storage process play an important role in determining the models accuracy. Thermal properties of tubers under storage available in literature are generally of those in semi processed form (skinless) such as those having undergone peeling, dicing and cutting actions. This study investigates the effect of tuber skin on the thermal properties of whole tubers of potato and sweet potato. A direct approach was used to measure the tubers' density and thermal conductivity and thermal diffusivity by the transient heat transfer method. Indirect approach was used to measure the tubers' specific heat. Experimental data were used to develop empirical models of the thermal coefficients as a function of temperature. Results of the study should find great use in the modeling of potato and sweet potato storage process

Classification of Retinal Images Based on Statistical Moments and Principal Component Analysis

Early diagnosis of Diabetic Retinopathy (DR) has been suggested as a good measure of preventing blindness associated with Diabetes. Some of the reported methodologies of Retinal Images (RI) classification for early diagnosis of DR have been shown to involve several steps and approaches for effective and accurate diagnosis. Thus, this paper investigates the classification of RI using a two-stage procedure. The first stage includes the extraction of blood vessels from RI belonging to healthy and diabetes retinal images using a modified local entropy thresholding algorithm. In the second stage, different features are extracted including statistical moments and principal components. The set of extracted features is combined into one feature vector and fed into a Sequential Minimal Optimization (SMO) classifier. The obtained result is encouraging with an average accuracy of 68.33 %

A new method of vascular point detection using artificial neural network

Vascular intersection is an important feature in retina fundus image (RFI). It can be used to monitor the progress of diabetes hence accurately determining vascular point is of utmost important. In this work a new method of vascular point detection using artificial neural network model has been proposed. The method uses a 5×5 window in order to detect the combination of bifurcation and crossover points in a retina fundus image. Simulated images have been used to train the artificial neural network and on convergence the network is used to test (RFI) from DRIVE database. Performance analysis of the system shows that ANN based technique achieves 100% accuracy on simulated images and minimum of 92% accuracy on RFI obtained from DRIVE database

Stability of Passive Immunisation and Efficacy of Vaccines of Hepatitis B Model

Abstract: The phenomenon of passive immunisation in disease control models, where a stable epidemic equilibrium state co exist with a stable disease free equilibrium when associated eigen values are all negative, has important implication for disease control. In this study, we modelled the effect of passive immunisation and infectious hepatitis B treatment on the spread and control of the disease. We established the existence of equilibrium states and analyse the disease free equilibrium for stability. It was established that 8 1 = -:, 8 2 = -:, 8 3 = -(( + :) and 8 4 = (*B / :) -: hence, the disease free equilibrium state will be stable if (*B / :) < : i.e., (number of susceptible individuals produced is less than natural death rate). Thus, effort should be intensify in increasing the duration of efficacy of the vaccines used in passive immunisation programme