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    • 1. 发明申请
    • MOTOR DRIVE CONTROL DEVICE
    • 电机驱动控制装置
    • US20100192788A1
    • 2010-08-05
    • US12698596
    • 2010-02-02
    • Yasuhiko TANAKAKenji SUZUKIHiroshi NAGASEToshihiko YAMAMOTOShunsuke MATSUNAGA
    • Yasuhiko TANAKAKenji SUZUKIHiroshi NAGASEToshihiko YAMAMOTOShunsuke MATSUNAGA
    • B30B1/42H02P27/00H02P3/14
    • H02M5/4585H02M1/36H02M7/797
    • A motor drive control device including a power supply converter that is connected to an alternating-current power supply, a voltage detector that detects a direct-current voltage of the power supply converter, an inverter that is connected to a motor, and an energy storage device that is connected to a direct-current bus that connects the power supply converter and the inverter. The motor drive control device includes: a power-running operation start voltage setting section that sets a power-running operation start voltage of the power supply converter; a power-running operation start control section that causes the power supply converter to start a power-running operation when the direct-current voltage of the power supply converter has reached the power-running operation start voltage set by the power-running operation start voltage setting section; a power-running operation stop voltage setting section that sets a power-running operation stop voltage of the power supply converter; and a power-running operation stop control section that causes the power supply converter to stop the power-running operation when the direct-current voltage of the power supply converter has reached the power-running operation stop voltage set by the power-running operation stop voltage setting section.
    • 一种电动机驱动控制装置,包括连接到交流电源的电源转换器,检测电源转换器的直流电压的电压检测器,连接到电动机的逆变器和能量存储器 设备连接到连接电源转换器和逆变器的直流总线。 所述电动机驱动控制装置包括:动力运行动作开始电压设定部,其设定所述电源转换器的动力运行动作开始电压; 电源运行运行开始控制部,当电源转换器的直流电压达到由电力运行运行开始电压设定的运转运行开始电压时,使电源转换器开始运行动作 设置部分 动力运行停止电压设定部,其设定所述电源转换器的动力运行停止电压; 以及动力运行停止控制部,其使得当电力供应转换器的直流电压达到由电力运行运行停止设定的动力运行停止电压时,电源转换器停止运行运行 电压设定部。
    • 4. 发明申请
    • STRUCTURE OF SOLID OXIDE FUEL CELL
    • 固体燃料电池结构
    • US20120107715A1
    • 2012-05-03
    • US13178889
    • 2011-07-08
    • Makoto OHMORIKunihiko YOSHIOKATakashi RYUKenji SUZUKI
    • Makoto OHMORIKunihiko YOSHIOKATakashi RYUKenji SUZUKI
    • H01M8/24H01M8/10
    • H01M8/1213H01M8/0273H01M8/1286H01M8/2425H01M8/2428H01M8/2457H01M2008/1293
    • On each of upper and lower surfaces of a flat-plate-like support substrate having a longitudinal direction and having fuel gas flow channels formed therein, a plurality of power-generating elements A connected electrically in series are disposed at predetermined intervals along the longitudinal direction. On each of the upper and lower surfaces of the support substrate, a plurality of recesses are formed at predetermined intervals along the longitudinal direction. Each of the recesses is a rectangular-parallelepiped-like depression defined by four side walls arranged in a circumferentially closed manner and a bottom wall. That is, in the support substrate, frames are formed to surround the respective recesses. Fuel electrodes of the power-generating elements A are embedded in the respective recesses, and inter connectors are embedded in respective recesses formed on the outer surfaces of the fuel electrodes.
    • 在具有长度方向的平板状支撑基板的上表面和下表面的每一个上形成有燃料气体流路的多个发电元件A沿着长度方向以预定的间隔配置, 。 在支撑基板的上表面和下表面的每一个上,沿着纵向方向以预定间隔形成多个凹部。 每个凹部是由周向封闭的方式布置的四个侧壁和底壁限定的矩形平行六面体形凹部。 也就是说,在支撑基板中,框架形成为围绕相应的凹部。 发电元件A的燃料电极嵌入在各个凹部中,并且将连接器嵌入形成在燃料电极的外表面上的各个凹部中。
    • 10. 发明申请
    • TRANSFORMING PROJECTION DATA IN TOMOGRAPHY BY MEANS OF MACHINE LEARNING
    • US20180018757A1
    • 2018-01-18
    • US15646119
    • 2017-07-11
    • Kenji SUZUKI
    • Kenji SUZUKI
    • G06T3/40A61B6/03G06F15/18
    • G06T3/4053A61B6/03A61B6/032A61B6/5205A61B6/563G06N3/0454G06N3/084G06N5/003G06N7/005G06N20/00G06N20/10G06N20/20G06T3/4046G06T7/11G06T11/008
    • A method and system for transforming low-quality projection data into higher quality projection data, using of a machine learning model. Regions are extracted from an input projection image acquired, for example, at a reduced x-ray radiation dose (lower-dose), and pixel values in the region are entered into the machine learning model as input. The output of the machine learning model is a region that corresponds to the input region. The output information is arranged to form an output high-quality projection image. A reconstruction algorithm reconstructs high-quality tomographic images from the output high-quality projection images. The machine learning model is trained with matched pairs of projection images, namely, input lower-quality (lower-dose) projection images together with corresponding desired higher-quality (higher-dose) projection images. Through the training, the machine learning model learns to transform lower-quality (lower-dose) projection images to higher-quality (higher-dose) projection images. Once trained, the trained machine learning model does not require the higher-quality (higher-dose) projection images anymore. When a new lower-quality (low radiation dose) projection image is entered, the trained machine learning model would output a region similar to its desired region, in other words, it would output simulated high-quality (high-dose) projection images where noise and artifacts due to low radiation dose are substantially reduced, i.e., a higher image quality. The reconstruction algorithm reconstructs simulated high-quality (high-dose) tomographic images from the output high-quality (high-dose) projection images. With the simulated high-quality (high-dose) tomographic images, the detectability of lesions and clinically important findings can be improved.