Electron-beam-induced soft-X-ray emission spectroscopy (SXES) that uses a grating spectrometer has been introduced to a conventional scanning electron microscope (SEM) for characterizing desired specimen areas of bulk materials. The spectrometer was designed as a grazing incidence flat-field optics by using aberration corrected (varied line spacing) gratings and a multichannel plate detector combined with a charge-coupled device camera, which has already been applied to a transmission electron microscope. The best resolution was confirmed as 0.13 eV at Mg L-emission (50 eV), which is comparable with that of recent dedicated electron energy-loss spectroscopy instruments. This SXES-SEM instrument presents density of states of simple metals of bulk Mg and Li. Apparent band-structure effects have been observed in Si L-emission of Si wafer, P L-emission of GaP wafer, and Al L-emissions of intermetallic compounds of AlCo, AlPd, Al2Pt, and Al2Au.

The oil and fat were treated at first by only ozone bubbling and it was confirmed that the collection efficiency of them became 98.4% when the aeration was used. It showed that the aeration method in a grease trap cleared the standard value of 90% and there was no worry on the oil and fat outflow from a grease trap. The characteristics of sonication process were studied for free fatty acids degradation. The free saturated fatty acids are the most hard-degradable compounds of the fats, oils and greases (FOGs) in the grease trap. The influence of various parameters such as immersion level of an ultrasound probe in the liquid and bubbling of various gases (Ar, O2, air, O3) on the sonochemical and energy efficiency of the sonication process was investigated. The most effective degradation treatment method for saturated free fatty acids was the combination of sonication and low flow rate argon bubbling.

This paper proposes a repetitive control type optimal gait generation framework by executing learning control and parameter tuning. We propose a learning optimal control method of Hamiltonian systems unifying iterative learning control (ILC) and iterative feedback tuning (IFT). It allows one to simultaneously obtain an optimal feedforward input and tuning parameter for a plant system, which minimizes a given cost function. In the proposed method, a virtual constraint by a potential energy prevents a biped robot from falling. The strength of the constraint is automatically mitigated by the IFT part of the proposed method, according to the progress of trajectory learning by the ILC part.

Double ion implanted 4H-SiC bipolar junction transistors (BJTs) are fabricated by Al and N ion implantation to the base and emitter. The current gain of 3 is obtained at the base Al concentration of 1 × ∼ 1017 /cm3. The collector current as a function of the base Gummel number suggests that double ion implanted 4H-SiC BJT operates in the intrinsic region below the emitter in the low injection level. The high base resistance restricts the base current at VBE as low as 3 V.