Joon Hur

Piezoelectric nanogenerators synthesized using KNbO3 nanowires with various crystal structures

KNbO3 (KN) nanowires having a tetragonal structure or a polymorphic phase boundary (PPB) structure, which contains both tetragonal (P4mm) and orthorhombic (Amm2) structures, are formed at low temperatures. The presence of tetragonal and PPB KN nanowires is attributed to the existence of OH− and H2O defects. Further, the tetragonal and PPB KN nanowires change to orthorhombic KN nanowires in the temperature range between 300 and 400 °C owing to desorption of the lattice hydroxyl group. A composite consisting of polydimethylsiloxane (PDMS) and KN nanowires having a PPB structure shows large dielectric constant and low dielectric loss values of 9.2 and 0.5%, respectively, at 100 kHz. Moreover, a nanogenerator (NG) synthesized using the PPB KN nanowires exhibits the largest output voltage and current among NGs synthesized using the tetragonal or orthorhombic KN nanowires. In particular, the NG containing 0.7 g of PPB KN nanowires shows an output voltage of 10.5 V and an output current of 1.3 μA; these values are among the highest ever reported for NGs synthesized using a lead-free composite. In addition, this NG exhibited the maximum output power and energy conversion efficiency, which were 4.5 μW and 0.9%, respectively, for an external load of 1.0 MΩ.

An intelligent manufacturing process diagnosis system using hybrid data mining

The high cost of maintaining a complex manufacturing process necessitates the enhancement of an efficient maintenance system. For the efficient maintenance of manufacturing process, precise diagnosis of the manufacturing process should be performed and the appropriate maintenance action should be executed when the current condition of the manufacturing system is diagnosed as being in abnormal condition. This paper suggests an intelligent manufacturing process diagnosis system using hybrid data mining. In this system, the cause-and-effect rules for the manufacturing process condition are inferred by hybrid decision tree/evolution strategies learning and the most effective maintenance action is recommended by a decision network and AHP (analytical hierarchy process). To verify the hybrid learning proposed in this paper, we compared the accuracy of the hybrid learning with that of the general decision tree learning algorithm (C4.5) and hybrid decision tree/genetic algorithm learning by using datasets from the well-known dataset repository at UCI (University of California at Irvine).