김동일 교수

In induction motor control, power efficiency is an important factor to be considered. We attempt to achieve both high dynamic performance and maximum power efficiency by means of linear decoupling of rotor speed (or motor torque) and rotor flux. The induction motor with our controller possesses the input-output dynamic characteristics of a linear system such that the rotor speed (or motor torque) and the rotor flux are decoupled. The rotor speed responses are not affected by abrupt changes in th

A proportional, integral, and derivative (PID) type iterative learning controller is proposed for precise tracking control of industrial robots and computer numerical controller (CNC) machine tools performing repetitive tasks. The convergence of the output error by the proposed learning controller is guaranteed under a certain condition even when the system parameters are not known exactly and unknown external disturbances exist. As the proposed learning controller is repeatedly applied to the i

This paper presents the development of a CNC (computer numerical controller) system with an Intel 80486 as the main CPU, a floating point DSP (digital signal processor) TMS320C31 as the motion control CPU, a graphic DSP TMS34020 as the graphic CPU, and a MC68000 as the CPU of the internal PLC (programmable logic controller). Through the milling center equipped with the developed CNC system, the dependence of machining accuracy of the machine tool equipped with the developed CNC system on the acc

The number of English Language Learners (ELLs) has been growing worldwide. ELLs are at risk for reading disabilities due to dual difficulties with linguistic and cultural factors. This raises the need for finding practical and efficient reading interventions for ELLs to improve their literacy development and English reading skills. The purpose of this study is to examine the evidence-based reading interventions for English Language Learners to identify the components that create the most effecti

Machine learning has been applied successfully for faulty wafer detection tasks in semiconductor manufacturing. For the tasks, prediction models are built with prior data to predict the quality of future wafers as a function of their precedent process parameters and measurements. In real-world problems, it is common for the data to have a portion of input variables that are irrelevant to the prediction of an output variable. The inclusion of many irrelevant variables negatively affects the perfo