Adang Suwandi Ahmad

Automated Flight Test and System Identification for Rotary Wing Small Aerial Platform using Frequency Responses Analysis

This paper proposes an autopilot system that can be used to control the small scale rotorcraft during the flight test for linear-frequency-domain system identification. The input frequency-sweep is generated automatically as part of the autopilot control command. Therefore the bandwidth coverage and consistency of the frequency-sweep are guaranteed to produce high quality data for system identification. Beside that, we can set the safety parameters during the flight test (maximum roll/pitch value, minimum altitude, etc.) so the safety of the whole flight test is guaranteed. This autopilot system is validated using hardware in the loop simulator for hover flight condition.

New resampling algorithm for particle filter localization for mobile robot with 3 ultrasonic sonar sensor

This paper present a particle filter also known as Monte Carlo Localization (MCL) to solve the localization problem presented before. A new resampling mechanism is proposed. This new resampling mechanism enables the particle filter to converge quicker and more robust to kidnaping problem. This particle filter is simulated in MATLAB and also experimented physically using a simple autonomous mobile robot built with Lego Mindstorms NXT with 3 ultrasonic sonar and RWTH Mindstorms NXT Toolbox for MATLAB to connect the robot to MATLAB. The particle filter with the new resampling algorithm can perform very well in the physical experiments.

Reinforcement learning with heuristic to solve POMDP problem in mobile robot path planning

In this paper we propose a method of presenting a special case of Value Function as a solution to POMDP in mobile robot navigation. By using this new method the Value Function complexity will be reduced and more intuitive. We also propose a new reinforcement learning method to solve the Value Function. This reinforcement learning is based on Bellman Equation augmented with A* like heuristic during update iteration. The result of this new Value Function is validated with This particle filter is simulaed in Matlab and also experimented physically using a simple autonomous mobile robot built with Lego Mindstorms NXT with 3 ultrasonic sonar and RWTH Mindstorms NXT Toolbox for Matlab to connect the robot to Matlab. This simulation and experiment also incorporate particle filter localization from previous research. The simulation and experiment show that the Value Function can be utilized very well.

Implementation Knowledge Growing System Algorithm using VHDL

Knowledge Growing System (KGS) is novel perspectives in Artificial Intelligence which emulate the way of human brain develop new knowledge from information which obtained from its sensory organs. Information Fusion is a process to combine a set of information from several observation which come from difference sources, to generate single information as a conclusion. In this paper we propose how to design VHDL program to execute KGSs Information Fusion Algorithm. Furthermore, the VHDL Program of Information Fusion will became the basis of designing a chip of processor call Cognitive Processor.

Cogitive Artificial Intelligence: The fusion of Artificial Intelligence and information fusion

Cognitive Artificial Intelligence (CAI) is a new perspective in Artificial Intelligence (AI), which brings a new concept of intelligence that is not only limited in the emulation of behavior and ways of how human thinks but also to explore how humans brain grows its knowledge. The development of CAI was started by devising new method that is able to mimick the humans brain ability to perform Knowledge Growing (KG). Based on our long and thorough observation, the knowledge development in human brain is carried out by fusing the information obtained by human sensory organs from the environment. New knowledge is obtained as the time passes by fusing new information with the knowledge already stored in the brains memory. In this paper we present a method which emulates the mechanism of KG within human brain called A3S (Arwin-Adang-Aciek-Sembiring). The system which uses this method is called as Knowledge Growing System (KGS). We also present an example with real-life data to show the work of A3S in performing KG.

Transformer end-of-life estimation using condition-Based Sampling Period Method

This paper discusses transformer end-of-life estimation using condition-Based Sampling Period method. Condition of the transformer is randomly generated. This condition will be used to determine the sampling period of the measurement data collected from sensors. The observation interval will be decreased if the condition of the transformer deteriorates fast. The tangent of the condition will be used to estimate transformer end-of-life. Simulation shows transformer end-of life can be estimated using this method and the next observation can be determined according to the gradient of condition.

Simplified Q-learning for holonomic mobile robot navigation

In this paper we propose a method of presenting a special case of Value Function as a solution to POMDP in holonomic mobile robot navigation. By using this new method the Value Function complexity will be reduced and more intuitive. The result of this new Value Function is validated with particle filter simulation in Matlab and also experimented physically using a simple autonomous mobile robot built with Lego Mindstorms NXT with 3 ultrasonic sonar and RWTH Mindstorms NXT Toolbox for Matlab to connect the robot to Matlab. This simulation and experiment also incorporate particle filter localization from previous research. The simulation and experiment show that the Value Function can be utilized very well.

Obtaining Knowledge of Genes’ Behavior in Genetic Regulatory System by Utilizing Multiagent Collaborative Computation

All living organisms are built from millions of cells that work together in a very systematic manner. The trait of each organism is determined by the cells. The function of a living cell is performed by a sequence of well-coordinated activities by a large number of genes. The gene itself is a sequence of Deoxyribonucleic Acid (DNA). The products of a cell are made by the proteins synthesized in accordance with the instructions contained in the DNA (Willett, 2006) since it encodes all the information required for the development and functioning of an organism (Emmert-Streib & Dehmer, 2008). The expression of a gene is a biological process which a DNA sequence is translated to become a protein. The protein is an important molecule for determining the structure, mortality, metabolism, signaling, reproduction, etc. of a cell. Some genes influence how other gene or genes are expressed. Modifying genes may change the affect of other genes. The expression of a gene or genes is regulated by a mechanism called Genetic Regulatory System (GRS). The GRS’ task is to control whether genes are active or inhibit. Of ways for understanding and analyzing the behavior of GRS is by using microarray data. Microarray is sets of miniaturized reaction areas that may also be used to test the binding of DNA fragment (Reece, 2004). and it exploits the preferential binding of complementary nucleic acid sequences to simultaneously measure expression levels of thousands of genes (Dill, Liu, & Grodzinski, 2009). By having knowledge regarding the influence of a gene or genes to the others in microarray data, we can make estimations of the genes behavior in GRS in order to obtain better products in the future. According to (Ewens & Grant, 2005), there are three basic questions that can be answered by using microarray data: a. What genes are expressed in a given sample? b. Which genes are differentially expressed between different samples? c. How can one find different classes, or clusters, of genes which are expressed in a correlated fashion across a set of samples? How can one find different classes of samples based on their gene expression behavior?

A new information-inferencing fusion method for intelligent agents

In this paper we address an approach on how to emulate humans capability in obtaining comprehensive inferencing after observing a phenomenon in its environment. Before making a decision or taking an action regarding a situation that is occurring or probably occurs in the future, human observes the situation dynamics by using his/her sensing organs and communicating with other people. Information collected by the sensing organs is delivered to the brain to be fused to obtain inferencing. Comprehensive inferencing will be obtained after the previous inferencing is fused with new inferencing obtained from other sources. This humans capability will bring a benefit to humankind if it can be emulated and applied to intelligent agents. For this purpose, we propose a new information-inferencing fusion method called A3S (Arwin-Adang-Aciek-Sembiring) as an emulation of human information-inferencing fusion mechanism.

Intelligent Multi-Agent Implementation for Finding Genetic Regulatory Systems of Mycobacterium Tuberculosis

Our concept of Intelligent Multi Agent brings a new paradigm in Distributed Computation System. Earlier models of distributed computation use close relationships among computers, while using this new paradigm of Intelligent Multi Agent, each computer is autonomous without any memory nor clock sharing.