Approximation Model Development and Dynamic Characteristic Analysis Based on Spindle Position of Machining Center.

To evaluate the dynamic characteristics at all positions of the main spindle of a machine tool, an experimental point was selected using a full factorial design, and a vibration test was conducted. Based on the measurement position, the resonant frequency was distributed from approximately 236 to 242 Hz. The approximation model was evaluated based on its resonant frequencies and dynamic stiffness using regression and interpolation methods.

Multi-Filter Clustering Fusion for Feature Selection in Rotating Machinery Fault Classification.

In the fault classification process, filter methods that sequentially remove unnecessary features have long been studied. However, the existing filter methods do not have guidelines on which, and how many, features are needed. This study developed a multi-filter clustering fusion (MFCF) technique, to effectively and efficiently select features.

A Deep-Learning Approach for Foot-Type Classification Using Heterogeneous Pressure Data.

The human foot is easily deformed owing to the innate form of the foot or an incorrect walking posture. Foot deformations not only pose a threat to foot health but also cause fatigue and pain when walking; therefore, accurate diagnoses of foot deformations are required. However, the measurement of foot deformities requires specialized personnel, and the objectivity of the diagnosis may be insufficient for professional medical personnel to assess foot deformations.

Earth-viewing satellite perspectives on the Chelyabinsk meteor event.

Large meteors (or superbolides [Ceplecha Z, et al. (1999) Meteoroids 1998:37-54]), although rare in recorded history, give sobering testimony to civilization's inherent vulnerability. A not-so-subtle reminder came on the morning of February 15, 2013, when a large meteoroid hurtled into the Earth's atmosphere, forming a superbolide near the city of Chelyabinsnk, Russia, ∼1,500 km east of Moscow, Russia [Ivanova MA, et al.