Reactor TRIGA PUSPATI (RTP) Mark II type undergoes safe operation for more than 30 years and the only research reactor exists in Malaysia. The main safety feature of Instrumentation and Control (I&C) system design is such that any failure in the electronic, or its associated components, does not lead to an uncontrolled rate of reactivity. The existed controller using feedback approach to control the reactor power. This paper introduces proposed controllers such as Model Reference Adaptive Control (MRAC) and Proportional Integral Derivatives (PID) controller for the RTP simulation. In RTP, the most important considered parameter is the reactor power and act as nervous system. To design a controller for complex plant like RTP is quite difficult due to high cost and safety factors cause by the failure of the controller. Furthermore, to overcome these problems, a simulator can be used to replace functions the hardware and test could then be simulated using this simulator. In order to find the best controller, several controllers were proposed and the result will be analysed for study the performances of the controller. The output result will be used to find out the best RTP power controller using MATLAB/Simulink and gives result as close as the real RTP performances. Currently, the structures of RTP was design using MATLAB/Simulink tool that consist of fission chamber, controller, control rod position, height-to-worth of control rods and a RTP model. The controller will control the control rod position to make sure that the reactivity still under the limitation parameter. The results given from each controller will be analysed and validated through experiment data collected from RTP.

Microarrays have been proven to be beneficial for understanding the genetics of disease. They are used to assess many different types of cancers. Machine learning algorithms, like the artificial neural network (ANN), can be trained to determine whether a microarray sample is cancerous or not. The classification is performed using the features of DNA microarray data, which are composed of thousands of gene values. However, most of the gene values have been proven to be uninformative and redundant. Meanwhile, the number of the samples is significantly smaller in comparison to the number of genes. Therefore, this paper proposed the use of a simulated Kalman filter with mutation (SKF-MUT) for the feature selection of microarray data to enhance the classification accuracy of ANN. The algorithm is based on a metaheuristics optimization algorithm, inspired by the famous Kalman filter estimator. The mutation operator is proposed to enhance the performance of the original SKF in the selection of microarray features. Eight different benchmark datasets were used, which comprised: diffuse large b-cell lymphomas (DLBCL); prostate cancer; lung cancer; leukemia cancer; “small, round blue cell tumor” (SRBCT); brain tumor; nine types of human tumors; and 11 types of human tumors. These consist of both binary and multiclass datasets. The accuracy is taken as the performance measurement by considering the confusion matrix. Based on the results, SKF-MUT effectively selected the number of features needed, leading toward a higher classification accuracy ranging from 95% to 100%.

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The braking system is the crucial part in vehicle system. The main purpose of braking system is to slow down or stop the moving vehicle. The regenerative braking system (RBS) designed to recapture more energy during braking. The electric vehicle dynamic model was design using Matlab/Simulink. Sliding mode controller with super-twisting (SMCST) was designed to avoid overcharging and improved the batteries’ SOC. Conventional sliding mode control (SMC) shows convergence within the desire level of accuracy, in which chattering is the main issue related to destructive phenomenon. SMCST intentionally to eliminate chattering with high accuracy. The results from the simulation show that the super-twisting control strategy offers higher regeneration efficiency.