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A multimodal imaging instrument has been developed that integrates scanning near‐field optical microscopy with nanofocused synchrotron X‐ray diffraction imaging. The instrument allows for the simultaneous nanoscale characterization of electronic/near‐field optical properties of materials together with their crystallographic structure, facilitating the investigation of local structure–property relationships. The design, implementation and operating procedures of this instrument are reported. The scientific capabilities are demonstrated in a proof‐of‐principle study of the insulator–metal phase transition in samarium sulfide (SmS) single crystals induced by applying mechanical pressure via a scanning tip. The multimodal imaging of an in situ tip‐written region shows that the near‐field optical reflectivity can be correlated with the heterogeneously transformed structure of the near‐surface region of the crystal. [ABSTRACT FROM AUTHOR]

LIGA process has been developed in the 2.5-dimensional world. We introduced new technologies of a 3D X-ray lithography and a worm injection molding with an unscrewing de-molding mechanism, and succeeded in the deployment of a 3D LIGA process. Furthermore, we fabricated a spiral microcoil using the 3D-LIGA process and a metallization technique combining flat and smooth electroplating and isotropic chemical etching. The microcoil diameter was 0.5 mm and the length was 1 mm. The width of coil lines was 10µm and the pitch was 20µm. Characteristics of this microcoil as an inductor combine the inductance of 91 nH and the quality factor of 5.8 at the frequency of 1 GHz. © 2008 Wiley Periodicals, Inc. Electr Eng Jpn, 166(1): 43–51, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20679 [ABSTRACT FROM AUTHOR]

This paper proposes a neural network for moving object recognition based on the visual system. The proposed network has a structure focusing on the parallel hierarchy of visual information processing and can recognize moving objects. The network is composed of a movement detection module, a moving object estimation module, and a moving object recognition module. The movement detection module uses a neural network which is a model of the motion recognition aspect of vision and recognizes motion. The moving object estimation module uses a hierarchical neural network with the backpropagation (BP) algorithm, and infers moving objects based on the features of the motion. The moving object recognition module uses a multiple-structured neural network which is obtained by improvement of the inhibition mechanism and recognizes moving objects on the basis of shape features. The information is transmitted from the movement detection module to the moving object recognition module through the moving object estimation module. With this mechanism, the motion recognition and pattern recognition processes are integrated, and moving objects can be recognized. A computer simulation is performed on the recognition of a walking human, and the effectiveness of the proposed network is demonstrated. © 2004 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 87(9): 46–55, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjb.20115 [ABSTRACT FROM AUTHOR]

In this paper, the authors propose a parallel-hierarchical neural network that can recognize multiple 3D objects from 2D projection images. The proposed network focuses on a parallel-hierarchical structure and memory-based recognition assistance, which are characteristics of the excellent vision systems that living organisms have, and refers to the neocognitron, which models the parallel-hierarchical structure of vision systems. The amount of calculations is reduced by deleting cells having a low degree of importance based on competition between cells and detecting features that differ for each cell. The network not only can recognize an object, but can also estimate its orientation at the same time. The memory-based recognition assistance is modeled by performing iterative processing during which the input image approaches a learning image based on the orientation estimation result. Weights are determined by sequentially presenting learning images, and no teaching signal is necessary. This is done in a short time since it is not an iterative learning method. The network performance was evaluated by using five objects from COIL-100. Images that were obtained by photographing each object after it was rotated 60° at a time around the vertical axis were used for the learning images. The experiments checked the recognition rates for various 2D projection images that were obtained from the 3D objects. These results verified the effectiveness of the proposed neural network as a 3D object recognition technique. © 2003 Wiley Periodicals, Inc. Syst Comp Jpn, 35(1): 1–12, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.10579 [ABSTRACT FROM AUTHOR]

A three-phase cage rotor induction machine works as an induction generator if it is driven by a prime mover and self-excited by a capacitor connected to its terminal and residual magnetism of the machine. The machine of this type is called a self-excited induction generator and is used mainly as an independent power source isolated from a commercial power network. The reactive output power of the induction generator is limited because the capacitance used for self-excitation is fixed. The article analyzes the experimental facts from theoretical viewpoints taking into account the core saturation characteristics of the machine. The relation between the machine and circuit constants and the control range is clarified.

An induction generator has been proposed that works stably over a wide-speed range. The only disadvantage of this generator is that a reactive power has to be supplied from a power source on the inverter side. This article proposes a means to solve the difficulty by connecting a dc chopper to the dc side of the inverter. The chopper works not only as a bypass circuit of dc current but also as a forced commutation circuit of inverter. A scheme is proposed to control the primary flux to keep the total efficiency maximum over wide ranges of generator speed and output power. In this case, the power factor of induction machine at low and output power level is also improved. A disadvantage of the proposed scheme is that the main circuit and the control circuit are a little complicated.

This article produces a simplified scheme in which the converter on the generator side controls both the reactive and real output powers without using the reactive power controller. The generator proposed in this article can be operated stably not only in steady state, but also in transient state. The induction generator is driven by a dc motor and these output power is converted to dc power, and dc power is converted to ac power of constant frequency through a separately excited inverter. The reactive power required for excitation of induction generator is supplied by the generator converter operating at control angle of advance.

In this paper, a novel version of the dual voltage converter is presented with improved voltage regulation and enabling a wide range of voltage control by using self-turn-off devices (power transistors) instead of diodes. Further, this system operates with unity displacement factor. We have obtained the analytical expressions of this circuit. The results are, however, quite complex. We could calculate values of the voltage and current waveforms by using a computer. The calculated values are essentially in agreement with the experimental values. Experimentally, an ideal load characteristics with null voltage regulation was obtained by the conduction angle control for power transistors. Further, a wide range of voltage from 220 V to 39 V was obtained. A disadvantage for this system is the requirement for snubber circuits with a larger value of capacitor in order to suppress the surge voltages a turn-off periods. [ABSTRACT FROM AUTHOR]