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    • 1. 发明授权
    • Learning and use of schemata in robotic devices
    • 学习和使用机器人设备中的模式
    • US08332070B2
    • 2012-12-11
    • US12619840
    • 2009-11-17
    • Claudius GläserFrank Joublin
    • Claudius GläserFrank Joublin
    • G05B19/04G05B15/00G05B13/02G06F19/00G06E1/00
    • B25J9/161G05B2219/33025G05B2219/33051G05B2219/39285G05B2219/39376
    • A robotic controller using schemata, the schemata being a set of parameterized sequences of motor commands in order to make a robot to achieve a set goal, the parameters of the sequences being gained from the state variables of the robotic controller, a robotic controller comprising an interface for supplying sensory input to the robotic controller. A schemata state memory (1) structure supplied with either input from a schemata recognition module (4) or input from an inverse model module (2) or combinations of them. An inverse model module (2) for generating motor commands based on state variables and stored schemata, a forward model module (3) for predicting state variables based on state variables and stored schemata, and a schemata recognition module (4) for selecting a schemata based on supplied state variables of the robot controlled by the robotic controller.
    • 使用模式的机器人控制器,所述模式是电机命令的一组参数化序列,以使机器人实现设定目标,所述序列的参数从所述机器人控制器的状态变量获得,所述机器人控制器包括: 用于向机器人控制器提供感觉输入的界面。 提供有来自模式识别模块(4)的输入或反模型模块(2)的输入或它们的组合的模式状态存储器(1)结构。 一种用于基于状态变量和存储模式生成电动机命令的逆模型模块(2),用于基于状态变量和存储模式预测状态变量的前向模型模块(3),以及用于选择模式的模式识别模块(4) 基于由机器人控制器控制的机器的提供的状态变量。
    • 2. 发明申请
    • LEARNING AND USE OF SCHEMATA IN ROBOTIC DEVICES
    • 在机器人设备中学习和使用SCHEMATA
    • US20100249999A1
    • 2010-09-30
    • US12619840
    • 2009-11-17
    • Claudius GläserFrank Joublin
    • Claudius GläserFrank Joublin
    • B25J9/00B25J19/02G06N3/02
    • B25J9/161G05B2219/33025G05B2219/33051G05B2219/39285G05B2219/39376
    • A robotic controller using schemata, the schemata being a set of parameterized sequences of motor commands in order to make a robot to achieve a set goal, the parameters of the sequences being gained from the state variables of the robotic controller, a robotic controller comprising an interface for supplying sensory input to the robotic controller. A schemata state memory (1) structure supplied with either input from a schemata recognition module (4) or input from an inverse model module (2) or combinations of them. An inverse model module (2) for generating motor commands based on state variables and stored schemata, a forward model module (3) for predicting state variables based on state variables and stored schemata, and a schemata recognition module (4) for selecting a schemata based on supplied state variables of the robot controlled by the robotic controller.
    • 使用模式的机器人控制器,所述模式是电机命令的一组参数化序列,以使机器人实现设定的目标,所述序列的参数从所述机器人控制器的状态变量获得,所述机器人控制器包括: 用于向机器人控制器提供感觉输入的界面。 提供有来自模式识别模块(4)的输入或反模型模块(2)的输入或它们的组合的模式状态存储器(1)结构。 一种用于基于状态变量和存储模式生成电动机命令的逆模型模块(2),用于基于状态变量和存储模式预测状态变量的前向模型模块(3),以及用于选择模式的模式识别模块(4) 基于由机器人控制器控制的机器的提供的状态变量。
    • 4. 发明授权
    • Using child directed speech to bootstrap a model based speech segmentation and recognition system
    • 使用子导向语音来引导基于模型的语音分割和识别系统
    • US08069042B2
    • 2011-11-29
    • US11859114
    • 2007-09-21
    • Frank JoublinHolger Brandl
    • Frank JoublinHolger Brandl
    • G10L15/06G10L15/04
    • G10L15/04G10L15/063G10L2015/027
    • A method and system for obtaining a pool of speech syllable models. The model pool is generated by first detecting a training segment using unsupervised speech segmentation or speech unit spotting. If the model pool is empty, a first speech syllable model is trained and added to the model pool. If the model pool is not empty, an existing model is determined from the model pool that best matches the training segment. Then the existing module is scored for the training segment. If the score is less than a predefined threshold, a new model for the training segment is created and added to the pool. If the score equals the threshold or is larger than the threshold, the training segment is used to improve or to re-estimate the model.
    • 一种用于获得语音音节模型池的方法和系统。 通过首先使用无监督的语音分割或语音单元识别来检测训练段来生成模型池。 如果模型池是空的,则训练第一个语音音节模型并将其添加到模型池中。 如果模型池不为空,则从模型池确定与训练段最匹配的模型池。 然后对现有的模块进行训练。 如果分数小于预定阈值,则创建训练段的新模型并将其添加到池中。 如果分数等于阈值或大于阈值,则训练段用于改进或重新估计模型。
    • 5. 发明授权
    • System and method for determining a common fundamental frequency of two harmonic signals via a distance comparison
    • 通过距离比较确定两个谐波信号的共同基频的系统和方法
    • US07895033B2
    • 2011-02-22
    • US11142879
    • 2005-05-31
    • Frank JoublinMartin Heckmann
    • Frank JoublinMartin Heckmann
    • G10L11/00
    • G10L25/48G10L15/20
    • One embodiment of the present invention provides a method of determining an evidence value capturing whether two band-pass signals are harmonics of a common fundamental frequency. A further embodiment of the present invention evaluates the distance between significant points of a signal such as a sinusoidal signal. One embodiment of the present invention provides a method of determining whether two or more band-pass signals are harmonics of a fundamental frequency, comprising evaluating a first distance between a first set of two or more significant points of a first band-pass signal, evaluating a second distance between a second set of two or more significant points of a second band-pass signal, and comparing the first distance to the second distance to determine whether the first and second signals are harmonics of the fundamental frequency.
    • 本发明的一个实施例提供一种确定两个带通信号是否为公共基频的谐波的证据值的方法。 本发明的另一实施例评估诸如正弦信号的信号的有效点之间的距离。 本发明的一个实施例提供了一种确定两个或更多个带通信号是否是基频的谐波的方法,包括评估第一带通信号的两个或更多有效点的第一组之间的第一距离,评估 在第二带通信号的两个或更多有效点的第二组之间的第二距离,并且将第一距离与第二距离进行比较,以确定第一和第二信号是否是基频的谐波。
    • 7. 发明申请
    • Increasing robustness of online calibration through motion detection
    • 通过运动检测提高在线校准的鲁棒性
    • US20070038398A1
    • 2007-02-15
    • US11501573
    • 2006-08-08
    • Frank JoublinTobias Rodemann
    • Frank JoublinTobias Rodemann
    • G06F19/00
    • B25J9/163B25J9/1697G05B19/401G05B2219/39367G05B2219/39387G05B2219/40617
    • In a method for controlling an orienting/positioning system, the orienting system comprises at least one sensor means, an external motion detection device for detecting an externally-caused motion in the environment of the sensor means and actuator means for controlling an orienting and/or positioning action of the sensor means. The method comprises the steps of: (a) Evaluating pre-action output information of said sensor means in order to detect the position of a pattern in the input space of the sensor means; (b) Deciding on a targeted post-action position of said pattern in the input space of said sensor means; (c) Defining a command for the actuator means by mapping any deviation of the pre-action position and the targeted post-action position in the input space coordinates of the sensor-means to actuator control coordinates using a predefined mapping function; (d) Orienting/Positioning the sensor means by the actuator means according to the defined command in order to carry out an orienting/positioning action; (e) Detecting the real post-action position of the patterns in the input space of said sensor means; (f) Detecting external motion of the pattern; and (g) Adapting the mapping function used in step (c), wherein steps (a) to (g) are repeated using the respectively adapted mapping function. An output signal from the external-motion detection device can be used to control the adaptation step (g).
    • 在一种用于控制定向/定位系统的方法中,定向系统包括至少一个传感器装置,外部运动检测装置,用于检测传感器装置的环境中的外部引起的运动,以及用于控制定向和/或 传感器装置的定位动作。 该方法包括以下步骤:(a)评估所述传感器装置的动作输出信息,以便检测传感器装置的输入空间中的图案的位置; (b)在所述传感器装置的输入空间中确定所述图案的目标后动作位置; (c)通过使用预定义的映射函数将传感器装置的输入空间坐标中的预动作位置和目标后动作位置的任何偏差映射到致动器控制坐标来定义致动器装置的命令; (d)根据所定义的命令通过致动器装置定向/定位传感器装置,以执行定向/定位动作; (e)检测在所述传感器装置的输入空间中的图案的真实的后动作位置; (f)检测图案的外部动作; 和(g)适应步骤(c)中使用的映射函数,其中使用分别适应的映射函数重复步骤(a)至(g)。 可以使用来自外部运动检测装置的输出信号来控制适应步骤(g)。
    • 8. 发明申请
    • Determination of a common fundamental frequency of harmonic signals
    • 确定谐波信号的共同基频
    • US20060195500A1
    • 2006-08-31
    • US11340918
    • 2006-01-26
    • Frank JoublinMartin Heckmann
    • Frank JoublinMartin Heckmann
    • G06F17/10
    • G10L25/90
    • Techniques are provided for determining the time course of the fundamental frequency of harmonic signals, wherein the input signal is split into different frequency channels by band pass filters. Distances between crossings of different orders are determined, and a histogram of all these distance values for each instant in time is calculated. The distance values build a peak at the distance corresponding to the fundamental frequency. An example application of this technique is separation of acoustic sound sources in monaural recordings based on their underlying fundamental frequency. Application of these techniques, however, is not limited to the field of acoustics. These techniques can also be applied to other signals such as those originating from pressure sensors.
    • 提供了用于确定谐波信号的基频的时间过程的技术,其中通过带通滤波器将输入信号分成不同的频率信道。 确定不同订单的交叉点之间的距离,并计算每个时刻的所有这些距离值的直方图。 距离值在与基频对应的距离处建立峰值。 该技术的一个示例应用是基于其基础频率在单声道录音中分离声学声源。 然而,这些技术的应用不限于声学领域。 这些技术也可以应用于诸如来自压力传感器的那些信号。