A grey-based Taguchi method is proposed to discover the optimal design of the rifle muzzle flash reducer. According to existing research and following interviews with specific domain experts, the critical design factors which influence the reducer's performance have been ascertained. They are: (1) existence of the front cover, (2) diameter of the outside exhaust hole, (3) location of the exhaust hole on the extension tube, (4) diameter of the outside tube and (5) length of the outside tube. Based on these factors and their different levels, the L 18 (2 1 *3 7 ) orthogonal array was chosen for experimental combinations. Analyses of the S/N ratio and grey matrix are presented to conduct verification through the obtained raw data from test firing. The proposed methodology confirms the stability of the optimal combination. Moreover, verification results boost one's confidence that the optimal reducer's parameters can achieve optimal quality. Under α = 0.05, the most significant factor for reducing muzzle flash is the existence of the front cover. Using such a reducer, with a combination designed in accordance with the Taguchi method, the muzzle flash with the reducer decreased the pixels from 94322 to 341, a 99.64% improvement. This is an extemely significant amelioration.

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The service robot described herein is an engineering application combining mechanical technology and computer science. For service robots, intelligence is the core competence. The humanoid intelligent service robot developed at Chienkuo Technology University will possess fi ve intelligent capabilities: marketing, advertisement, acceptance, guidance, and security. It must therefore be taught to obtain intelligence. The purpose of this research is to describe how an instructor teaches the humanoid intelligent service robot. First, according to the required basic action and distinct service script, the robot instructor demonstrates the basic human action. The digital 3D motion capture system captures these motions via video. The movements of the robot instructor's limbs and trunk will be digitized, encoded, and stored in the robot's intelligent computer memory. When service instructions activate the robot, it will replay the motions taught using 27 DC motors, 5 DC servo motors, and 10 solenoids.

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