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    • 94. 发明申请
    • Image forming apparatus and fixing device
    • 图像形成装置和定影装置
    • US20110091253A1
    • 2011-04-21
    • US12926975
    • 2010-12-21
    • Hiroshi SeoAkiko ItoTadashi OgawaToshiaki Higaya
    • Hiroshi SeoAkiko ItoTadashi OgawaToshiaki Higaya
    • G03G15/20
    • G03G15/2042G03G2215/2032
    • An image forming apparatus includes an image carrier to carry a toner image and a fixing device to fix the toner image transferred from the image carrier onto a recording medium by applying at least heat to at least one of the toner image and the recording medium. Such a fixing device includes: a magnetic flux generator to generate a magnetic flux; and a heat generating member disposed at least partially in the magnetic flux. The heat generating member includes a heat generating layer to generate heat via eddy currents therein induced by the magnetic flux, magnitudes of the eddy currents varying according to positions thereof in a width direction of the heat generating layer. Included within the heat generating layer is a magnetic layer having a Curie point in a range, e.g., from about 100 degrees centigrade to about 300 degrees centigrade.
    • 图像形成装置包括用于承载调色剂图像的图像载体和定影装置,用于通过至少向其中一个调色剂图像和记录介质施加热量将从图像载体转印的调色剂图像定影到记录介质上。 这种定影装置包括:磁通发生器,用于产生磁通量; 以及至少部分地设置在所述磁通中的发热部件。 发热元件包括通过其中由磁通引起的涡流产生热的发热层,涡流的大小根据其在发热层的宽度方向上的位置而变化。 包含在发热层内的是磁性层,其居里点在例如约100摄氏度至约300摄氏度的范围内。
    • 97. 发明授权
    • Voice recognition system
    • 语音识别系统
    • US07016837B2
    • 2006-03-21
    • US09953905
    • 2001-09-18
    • Hiroshi SeoMitsuya KomamuraSoichi Toyama
    • Hiroshi SeoMitsuya KomamuraSoichi Toyama
    • G10L15/20
    • G10L15/20G10L15/142
    • An initial combination HMM 16 is generated from a voice HMM 10 having multiplicative distortions and an initial noise HMM of additive noise, and at the same time, a Jacobian matrix J is calculated by a Jacobian matrix calculating section 19. Noise variation Namh (cep), in which an estimated value Ha^(cep) of the multiplicative distortions that are obtained from voice that is actually uttered, additive noise Na(cep) that is obtained in a non-utterance period, and additive noise Nm(cep) of the initial noise HMM 17 are combined, is multiplied by a Jacobian matrix, wherein the result of the multiplication and initial combination HMM 16 are combined, and an adaptive HMM 26 is generated. Thereby, an adaptive HMM 26 that is matched to the observation value series RNah(cep) generated from actual utterance voice can be generated in advance. When performing voice recognition by collating the observation value series RNah(cep) with adaptive HMM 26, influences due to the multiplicative distortions and additive distortions are counterbalanced, wherein an effect that is equivalent to a case where voice recognition is carried out with clean voice can be obtained, and a robust voice recognition system can be achieved.
    • 从具有乘法失真的语音HMM 10和加性噪声的初始噪声HMM生成初始组合HMM 16,同时由雅可比矩阵计算部分19计算雅可比矩阵J.噪声变化Namh(cep) ,其中从实际发出的语音获得的乘法失真的估计值Ha ^(cep),在非话语周期中获得的加性噪声​​Na(cep)和加法噪声Nm(cep) 初始噪声HMM 17被组合,乘以雅可比矩阵,其中乘法和初始组合HMM 16的结果被组合,并且生成自适应HMM 26。 由此,可以预先生成与实际的话语语音产生的观察值序列RNah(cep)相匹配的自适应HMM26。 通过将观测值序列RNah(cep)与自适应HMM26进行比较来进行语音识别时,由于乘法失真和附加失真引起的影响被平衡,其中与用干净的语音执行语音识别的情况相当的效果可以 并且可以实现鲁棒的语音识别系统。