S. P. Koh

A routing protocol based on trusted and shortest path selection for mobile ad hoc network

A mobile ad-hoc network (MANET) is a peer-to-peer wireless network where nodes can communicate with each other without the use of infrastructure such as access points or base stations. Nodes can join and leave the network at anytime and are free to move randomly and organize themselves arbitrarily. Due to this nature of MANET, it is possible that there could be some malicious and selfish nodes that try compromise the routing protocol functionality and makes MANET vulnerable to security attacks. In this paper, we present a security-enhanced AODV (Ad hoc On-demand Distance Vector Routing) routing protocol called R-AODV (Reliant Ad hoc On-demand Distance Vector Routing). The implementation of this work is done by modified a trust mechanism known as direct and recommendations trust model and then incorporating it inside AODV which will allow AODV to not just find the shortest path, but instead to find a short path that can be trusted. This enhances security by ensuring that data does not go through malicious nodes that have been known to misbehave. The R-AODV protocol has been implemented and simulated on NS-2. Based on the simulation result, it can be shown that R-AODV does provide a more reliable data transfer compared to the normal AODV if there are malicious nodes in the MANET.

Modified Direct Torque Control using Algorithm Control of Stator Flux Estimation and Space Vector Modulation Based on Fuzzy Logic Control for Achieving High Performance from Induction Motors

Direct torque control based on space vector modulation (SVM-DTC) protects the DTC transient merits. Furthermore, it creates better quality steady-state performance in a wide speed range. The modified method of DTC using SVM improves the electrical magnitudes of asynchronous machines, such as minimizing the stator current distortions, the stator flux with electromagnetic torque without ripple, the fast response of the rotor speed, and the constant switching frequency. In this paper, the proposed method is based on two new control strategies for direct torque control with space vector modulation. First, fuzzy logic control is used instead of the PI torque and a PI flux controller to minimizing the torque error and to achieve a constant switching frequency. The voltages in the direct and quadratic reference frame (V d ,V q ) d q are achieved by fuzzy logic control. In this scheme, the switching capability of the inverter is fully utilized, which improves the system performance. Second, the close loop of stator flux estimation based on the voltage model and a low pass filter is used to counteract the drawbacks in the open loop of the stator flux such as the problems saturation and dc drift. The response of this new control strategy is compared with DTC-SVM. The experimental and simulation results demonstrate that the proposed control topology outperforms the conventional DTC-SVM in terms of system robustness and eliminating the bad outcome of dc-offset.

A hybrid artificial immune systems for multimodal function optimization and its application in engineering problem

Lately, the field of Artificial Immune Systems (AIS) has attracted wide attention among researchers as the algorithm is able to improve local searching ability and efficiency. However, the rate of convergence for AIS is rather slow as compared to other Evolutionary Algorithms. Alternatively, Particle Swarm Optimization (PSO) has been used effectively in solving complicated optimization problems with simple coding and lesser parameters, but it tends to converge prematurely. Thus, the good features of AIS and PSO are combined in order to reduce their shortcomings. By comparing the optimization results of the mathematical functions and the engineering problem using hybrid AIS (HAIS) and AIS, it is observed that HAIS has better performances in terms of accuracy, convergence rate and stability.

Hybrid Artificial Immune System-Genetic Algorithm optimization based on mathematical test functions

This paper demonstrates a hybrid between two optimization methods that are Artificial Immune System (AIS) and Genetic Algorithm (GA). The capability of overcoming the shortcomings of individual algorithms without losing their advantages makes the hybrid techniques superior to the stand-alone ones based on the dominant purpose of hybridization. The improvement of the results that enable to get it if GA and AIS work separately is the main objective of this hybrid. The hybrid includes two processes; firstly, AIS is the attraction among the researchers as the algorithm. This enables it to develop local searching ability and efficiency yet the convergence rate for AIS is preferably not precise compared to the GA. Secondly, a Genetic Algorithm is typically initializing population randomly. The last generation of AIS will be the input to the next process of the hybrid which is the GA in this hybrid AIS-GA. Hybrid makes GA enters the stage of standard solutions more rapidly and more accurate compared with GA initialized population at random. To differentiate between the results in terms of achieving the minimum value for these functions, eight mathematical test functions are being used to make comparison.

Mathematical Function Optimization using AIS Antibody Remainder method

Artificial immune system (AIS) is one of the metaheuristics used for solving combinatorial optimization problems. In AIS, clonal selection algorithm (CSA) has good global searching capability. However, the CSA convergence and accuracy can be improved further because the hypermutation in CSA itself cannot always guarantee a better solution. Alternatively, Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been used efficiently in solving complex optimization problems, but they have a tendency to converge prematurely. In this study, the CSA is modified using the best solutions for each exposure (iteration) namely Single Best Remainder (SBR) - CSA. The results show that the proposed algorithm is able to improve the conventional CSA in terms of accuracy and stability for single and multi objective functions.

Using Electromagnetism-like algorithm and genetic algorithm to optimize time of task scheduling for dual manipulators

A method based on Electromagnetism-Like algorithm (EM) and Genetic Algorithm (GA) is proposed to determine the time-optimal task scheduling for dual robot manipulators. GA is utilized to calculate the near-optimal task scheduling for the two robots. On top of that, the EM is recommended as a suitable alternative to obtain multiple solutions at each task points for both manipulators with less error. During the course of the tour, the dual robots move from point to point with less cycle time, while ensuring that no collision occurs between the two manipulators or between the dual manipulators and the static obstacles in the workspace. The movement and the configurations of the manipulators at the task points were visualized using a simulator developed via Visual Basic. Net. The method is verified using two simulators acting as examples for two identical four-link planar robots working in the environment, with square-shaped obstacles cluttered at different locations.

A SWARM-BASED ARTIFICIAL IMMUNE SYSTEM FOR SOLVING MULTIMODAL FUNCTIONS

Artificial Immune Systems (AIS) have attracted enormous attention among researchers because the algorithms are able to improve global searching ability and efficiency. Nevertheless, the rate of convergence for AIS is relatively slow compared to other metaheuristic algorithms. On the other hand, genetic algorithms (GAs) and particle swarm optimization (PSO) have been used successfully in solving optimization problems, although they tend to converge prematurely. Therefore, the good attributes of AIS and PSO are merged in order to reduce this limitation. It is observed that the proposed hybrid AIS (HAIS) achieved better performances in terms of convergence rate, accuracy, and stability against GA and AIS by comparing the optimization results of the mathematical functions. A similar result was achieved by HAIS in the engineering problem when compared to GA, PSO, and AIS.

An improved artificial immune system based on antibody remainder method for mathematical function optimization

Artificial immune system (AIS) is one of the nature-inspired algorithm for optimization problem. In AIS, clonal selection algorithm (CSA) is able to improve global searching ability. However, the CSA convergence and accuracy can be improved further because the hypermutation in CSA itself cannot always guarantee a better solution. Alternatively, Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been used efficiently in solving complex optimization problems, but they have a tendency to converge prematurely. In this study, the CSA is modified using the best solutions for each exposure (iteration) namely Remainder-CSA. The results show that the proposed algorithm is able to improve the conventional CSA in terms of accuracy and stability for single objective functions.

Path control of dexterous robotic hand using Genetic Algorithm

This paper would presents the path control of a dexterous robotic hand finger reaching in a given search space. The proposed system would adopt the advantages of Genetic Algorithm (GA) to optimize the system performance in terms of path control, speed and accuracy. The system would search for a valid path and optimal velocity. A new genetic operator, namely Real Coded Dynamic Multilayer Chromosome Crossover (RC_DMCC) has been introduced and incorporated in the system. The simulation results of the proposed technique are presented.

An automated gravimetric & PWM based fluid dispensing system with GA parameter fine tuning

In this paper we present a gravimetric and Pulse Width Modulation (PWM) based fluid dispensing technique for a maximum dispense batch of 30kg. In addition, a Genetic Algorithm (GA) Parameter Fine Tuning technique is presented as well. Based on the combination of both techniques, the system is able to dispense up to 50 samples with an accuracy of +/− 2g with the dispensing speed varies between 50 seconds to 60 seconds for a 5.2kg batch within one dispense valve. The reported dispensing technique is based on PWM technique in the dispensing sequence and GA technique in the parameter fine tuning. This technique could overcome limitations of the volumetric dispensing and manual parameter tuning presently applied in the coatings industry. The fast and accurate system which is modularly built out of individual dispense valve is able to handle different fluids with varying viscosity. The working principles of the system as well as its accuracy results are presented.