Wahidin Wahab

Autonomous mobile robot navigation using a dual artificial neural network

This paper deals with an intelligent control of an autonomous mobile robot which should move safely in an environment to find a target. We describe our approach to solve the motion-planning problem in mobile robot control using artificial neural networks technique. The algorithm constructs a collision-free path for moving robot among obstacles based on two neural networks. The first neural network is used to determine the free space needed to avoid obstacles. The second neural network is used to navigate robot into target. Simulation examples is presented at the end of the paper.

Enhanced Individualization of Head-Related Impulse Response Model in Horizontal Plane Based on Multiple Regression Analysis

One key issue in modeling head-related impulse responses (HRIRs) is how to individualize HRIRs model so that it is suitable for a listener. The objective of this research is to establish multiple regression models between minimum phase HRIRs and the anthropometric parameters in order to individualize a given listener’s HRIRs with his or her own anthropometric parameters. We modeled the entire minimum phase HRIRs in horizontal plane of 37 subjects using principal components analysis (PCA). The individual minimum phase HRIRs can be estimated adequately by a linear combination of ten orthonormal basis functions. We proposed an enhanced individualization method based on multiple regression analysis of weights of basis functions by utilizing eight anthropometric parameters. Our objective simulation’s results show that the estimated minimum phase HRIRs have small error and can be perceived similarly as the measured ones. In addition, the subjective localization performance of the estimated HRIRs is improved compared to the measured HRIRs.

Comparison of Neural Networks Based Direct Inverse Control Systems for a Double Propeller Boat Model

This paper presents the thorough evaluation and analysis on the direct inverse neural networks based controller systems for a double-propeller boat model. Two direct inverse controller systems that were designed with and without feedback were implemented on a double propeller boat model using two neural networks based control approaches, namely the back-propagation based neural controller (BPNN-controller) and the self-organizing maps based neural controller (SOM-controller). Then, the resulted control errors of the systems were compared. Simulation results revealed that the direct inverse control without feedback produced lower error compared to the direct inverse control with feedback. Another important finding from the study was that the SOM-controller is superior to the BPNN-controller in terms of control error and training computational cost.

Moving Object Tracking Method Based on n-Step-ahead Prediction Using Artificial Neural Network Algorithm

This paper described a method of tracking a moving object based on 1 to 5 step ahead prediction. The prediction was using the artificial neural network with back propagation method for training the network. The moving object used in the experiments is a small table tennis ball. The ANN structures have six inputs neurons and five outputs neurons with ten neurons in the hidden layer. Using 70% data of the object movement positions for training, and 30% data for testing the prediction of the ball positions. It was shown that the training of the ANN can achieved means square error (MSE) as small as 0.0091 for the X coordinate and 0.0012 for the Y coordinate. At the ball position prediction testing, it was shown that the method can achieved the MSE of 4.72% for X coordinate and MSE of 2.48% for Y coordinate.

Particle Swarm Optimization-Based Direct Inverse Control for Controlling the Power Level of the Indonesian Multipurpose Reactor

A neural network-direct inverse control (NN-DIC) has been simulated to automatically control the power level of nuclear reactors. This method has been tested on an Indonesian pool type multipurpose reactor, namely, Reaktor Serba Guna-GA Siwabessy (RSG-GAS). The result confirmed that this method still cannot minimize errors and shorten the learning process time. A new method is therefore needed which will improve the performance of the DIC. The objective of this study is to develop a particle swarm optimization-based direct inverse control (PSO-DIC) to overcome the weaknesses of the NN-DIC. In the proposed PSO-DIC, the PSO algorithm is integrated into the DIC technique to train the weights of the DIC controller. This integration is able to accelerate the learning process. To improve the performance of the system identification, a backpropagation (BP) algorithm is introduced into the PSO algorithm. To show the feasibility and effectiveness of this proposed PSO-DIC technique, a case study on power level control of RSG-GAS is performed. The simulation results confirm that the PSO-DIC has better performance than NN-DIC. The new developed PSO-DIC has smaller steady-state error and less overshoot and oscillation.

Pengaruh Perubahan Set Point pada Pengendali Fuzzy Logic untuk Pengendalian Versi online (e-ISSN. 2252-620x) Suhu Mini Boiler

In this research, a mini boiler temperature control system is designed by using fuzzy logic controller (FLC). The FLC controls the valve of the incoming fuel. The mini boiler is fueled by gas, has length of 80 cm and diameter of 40 cm. FLC is designed in four different models based on the number of membership function of the temperature variable, i.e., three, five, seven and nine membership functions. The input variables are “temperature” and “error”, and the output variable is “valve”. There are two types of disturbance given to the control system, the disturbance of the system working at set point 125 °C, and disturbance by changing the set point values. In the first type, the FLC is able to reach 125 °C for all models. In the second type, the set points are varied to 100 °C, and 150 °C. At set point 125 °C and 150 °C, the FLC is able to achieve the pre-determined set points for all models. Mean while at set point 100 °C, the FLC can stabilized the system at point of 97.92 °C for the first model, and at the point of 100 °C for other models.

Assesment of Quality Classification of Green Pellets for Nuclear Power Plants Using Improved Levenberg-Marquardt Algorithm

Cylindrical uranium dioxide pellets, which are the main components for nuclear fuel elements in Light Water Reactor, should have a high density profile, uniform shape and quality for the safety used as a reactor fuel component. The quality of green pellets is conventionally monitored through a laboratory measurement of the physical pellets characteristics followed by a graphical chart classification technique. However, this conventional classification method shows some drawbacks, such as the difficulties on its usage, low accuracy and time consuming, and does not have the ability to adress the non-linearity and the complexity of the relationship between the pellet’s quality variables and the pellett’s quality. In this paper, an Improved Levenberg-Marquard based neural networks is used to classify the quality process of the green pellets. Robustness of this learning algorithm is evaluated by comparing its recognition rate to that of the conventional Back Propagation neural learning algorithm. Results show that the Improved Levenberg-Marquard algorithm outperformed the Back Propagation learning algorthm for various percentage of training/testing paradigm, showing that this system could be applied effectively for classification of pellet quality.

Foreword from TENCON 2011 organizing chair

It is my great pleasure to welcome all of you to the IEEE TENCON 2011 in Bali - Indonesia. The IEEE TENCON is the Regional 10 annual conference hosted by the IEEE Region 10 which covers the Asia Pacific regional members, which is the largest geographical coverage in the IEEE, and it is organized by the IEEE Indonesia Section in collaboration with the Department of Electrical Engineering University of Indonesia.