An application of contra-rotating rotors has been proposed against a demand for developing higher specific speed axial flow pump. The blade rows interaction between front and rear rotors should be clarified for its stable operation and reduction of unsteady losses. In this paper, the static pressure distributions on casing wall are provided by measuring with the phase locked sampling method. The measurements are carried out for two types of the rear rotors with different blade number and chord length, and it is found that, for both types of rotors, the unsteady pressure fluctuations are more remarkable in the front rotor than in the rear rotor and they are caused by the rear rotor pressure field. The effects of pressure fluctuations will be discussed in more details toward understanding the blade rows interaction in the contra-rotating axial flow pump.
An application of contra-rotating rotors has been proposed against a demand for developing higher specific speed axial flow pump with more compact structure, higher efficiency and higher cavitation performance. The blade rows interaction between front and rear rotors should be taken into account in pump design for stable operation and reduction of unsteady losses. The measurements of static pressure distributions on casing wall with the phase locked sampling method are carried out for two types of the rear rotors. In the present paper the difference of the rear blade interaction and the unsteady pressure fluctuation are clarified. The pressure fluctuations are more remarkable in the front rotor than in the rear rotor and they are caused by rear rotor pressure field. The effects of pressure fluctuations will be discussed in more details toward understanding the blade rows interaction in the contra-rotating axial flow pump.
A face recognition algorithm for LSI implementation suitable for embedded applications is presented. Although many algorithms to recognize a face [1,2,6]recently suggested have a relatively stable performance under the variety of environmental disturbances, the problem still lies on computational cost as well as memory usage. In this paper, we will propose an algorithm based on a "Pseudo Fisherface Matrix", which is derived from generic datasets and down-sampled Gabor features, which reduces these costs. We will compare performance of the proposed method with a traditional one based on Eigenface through FERET database. An experimental implementation demonstrated the proposed algorithm draws significant performance rations.
A wireless smart-scales system with a face recognition function has been developed as an application for wireless sensor networks. The face recognition employs a wireless camera; and the system automatically identifies a person and stores the weights of all the people that use the system on a server. Two key ultralow-energy circuit techniques were devised for the smart scales. One is a nearly-zero-standby-current circuit that combines a mechanical switch and an electrical CPU-controlled power switch; it reduces the standby power dissipation of the CPU from 1.5 mW to less than 0.1 µW. The other is a super-intermittently-operating circuit with a power-switch transistor and a small resistance; it suppresses the energy dissipation of the wireless camera to just 1/4 of the total energy dissipation. Furthermore, an electromechanical micropower generator with electromagnetic induction further reduces the energy dissipation. It is located under the scales and supplies a power of 75 mW during one second.
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