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    • 1. 发明授权
    • Method and apparatus for indetification, forecast, and control of a
non-linear flow on a physical system network using a neural network
    • 使用神经网络对物理系统网络上的非线性流进行独特,预测和控制的方法和装置
    • US5404423A
    • 1995-04-04
    • US48793
    • 1993-04-16
    • Tadasu UchiyamaNoboru SoneharaYukio Tokunaga
    • Tadasu UchiyamaNoboru SoneharaYukio Tokunaga
    • G05B13/02G05B23/02G06F15/18
    • G05B13/027G05B17/02
    • A neural network system for identifying forecasting, and controlling a non-linear flow on a physical system network, in which each branch between nodes in the physical system network is divided by a plurality of division points; a flow at each of the division points and a terminal point of each branch is calculated according to neural network model parameters specifying connections among the division points and the terminal point in a neural network model; an actual flow is measured at the terminal point of said each branch; an error of the calculated flow at the terminal point with respect to the measured actual flow at the terminal point is calculated; the neural network model parameters are adjusted to minimize the calculated error; and system dynamics parameters specifying dynamics of the physical system are determined according to the adjusted neural network model parameters. In addition, a target function to be optimized is calculated in terms of flows at terminal points of branches as a function of a control parameter specifying connecting and disconnecting of connections among branches at each node; and connections among branches at each node are connected/disconnected to optimize the calculated target function.
    • 一种用于识别预测和控制物理系统网络上的非线性流的神经网络系统,其中物理系统网络中的节点之间的每个分支被多个分割点分割; 根据神经网络模型参数,在神经网络模型中指定分割点和终点之间的连接来计算每个分支点的每个分支和每个分支的终点的流程; 在所述每个分支的终点处测量实际流量; 计算终点处的计算流量相对于终点处测量的实际流量的误差; 调整神经网络模型参数以最小化计算出的误差; 并根据调整后的神经网络模型参数确定指定物理系统动力学的系统动力学参数。 此外,根据指定在每个节点的分支之间的连接的连接和断开的控制参数的函数,根据分支终端处的流量来计算要优化的目标函数; 并且连接/断开每个节点处的分支之间的连接,以优化计算出的目标函数。
    • 2. 发明授权
    • Handwritting information detecting method and apparatus detachably
holding writing tool
    • 手写信息检测方法和装置可拆卸地保持书写工具
    • US5781661A
    • 1998-07-14
    • US495837
    • 1995-06-28
    • Akira HiraiwaMasaaki FukumotoTadasu UchiyamaNoboru SoneharaShigeru Oikawa
    • Akira HiraiwaMasaaki FukumotoTadasu UchiyamaNoboru SoneharaShigeru Oikawa
    • G06K9/00
    • G06K9/00154
    • The purpose of the present invention is to provide a handwriting information detecting method and apparatus for the same, in which a user can choose any handwriting tool such as a pen or a pencil, and when the user writes with the tool, the handwriting information can be detected. The handwriting information detecting method of the present invention comprises steps of detecting a motion of a writing tool, held by a writing tool holder comprising at least one acceleration sensor, based on at least one acceleration signal output from the at least one acceleration sensor when the writing tool is grasped by fingers; and recognizing handwriting information of a character or a figure written by the writing tool according to the detected motion thereof, and outputting the information. Preferably, the step of detecting a motion of a writing tool further comprises steps of detecting pressure acting on the tip of a finger which grasps the writing tool; and judging the motion of the writing tool according to the detected pressure. The handwriting information detecting apparatus of the present invention comprises a writing tool holder for detachably holding a writing tool; at least one acceleration sensor, provided at the writing tool holder, for detecting acceleration of the writing tool which is grasped by fingers; and a handwriting information recognition circuit for recognizing information of handwriting performed by the writing tool based on the output of the at least one acceleration sensor.
    • 本发明的目的是提供一种手写信息检测方法和装置,其中用户可以选择诸如笔或铅笔的任何手写工具,并且当用户用工具写入时,手写信息可以 被检测。 本发明的手写信息检测方法包括以下步骤:基于从至少一个加速度传感器输出的至少一个加速度信号,检测由包括至少一个加速度传感器的书写工具保持器保持的书写工具的运动,当至少一个加速度传感器 书写工具由手指掌握; 以及根据检测到的运动识别由书写工具书写的字符或图形的笔迹信息,并输出该信息。 优选地,检测书写工具的运动的步骤还包括检测作用在抓握书写工具的手指的尖端上的压力的步骤; 以及根据检测到的压力来判断书写工具的运动。 本发明的手写信息检测装置包括:用于可拆卸地保持书写工具的书写工具夹具; 设置在所述书写工具架上的至少一个加速度传感器,用于检测由手指抓握的书写工具的加速度; 以及手写信息识别电路,用于基于所述至少一个加速度传感器的输出来识别由所述书写工具执行的手写信息。
    • 3. 发明授权
    • Full-time wearable information managing device and method for the same
    • 全职可穿戴信息管理设备及方法相同
    • US06380923B1
    • 2002-04-30
    • US08298552
    • 1994-08-30
    • Masaaki FukumotoAkira HiraiwaTadasu UchiyamaNoboru Sonehara
    • Masaaki FukumotoAkira HiraiwaTadasu UchiyamaNoboru Sonehara
    • G09G500
    • G06F3/015G06F1/163G06F3/011G06F3/014G06F3/017G06F3/0235
    • A full-time wearable input device, method for the same is provided, wherein the immediate accessibility is improved without sacrificing the operational integrity, and information can be inputted anytime, anywhere, and immediately. The full-time wearable input device comprises a detector for detecting the shock generated at the time of striking the fingertips against a physical surface, and an analyzer for analyzing the timing at which the fingertips strike the aforementioned physical surface and determining the input information based on the detection signal detected by the aforementioned detector. As the above-mentioned detector, a shock sensor, acceleration sensor, sound sensor and myoelectric sensor can be used. The input information is then determined based on the change in the shock, acceleration, sound and/or myoelectric potential as detected by the sensors which are worn on each finger, wrist, or arm.
    • 提供了一种专用的可穿戴式输入装置,其方法在不牺牲运行完整性的情况下提高即时可达性,并可以随时随地,立即输入信息。 全时穿戴式输入装置包括用于检测在指尖撞击物理表面时产生的冲击的检测器和用于分析指尖撞击上述物理表面的定时的分析器,并且基于 由上述检测器检测出的检测信号。 作为上述检测器,可以使用冲击传感器,加速度传感器,声音传感器和肌电传感器。 然后基于穿在每个手指,手腕或手臂上的传感器检测到的冲击,加速度,声音和/或肌电位的变化来确定输入信息。
    • 4. 发明授权
    • Character recognition method
    • 字符识别方法
    • US5745599A
    • 1998-04-28
    • US374107
    • 1995-01-18
    • Tadasu UchiyamaNoboru SoneharaAkira Hiraiwa
    • Tadasu UchiyamaNoboru SoneharaAkira Hiraiwa
    • G06K9/22G06K9/48
    • G06K9/00429G06K9/00416
    • The present invention provides a handwritten character recognition method in which neighboring points are connected by straight lines and the segments in-between points are then interpolated using broken lines. Each broken line is equally divided into k segments, and a number is assigned to each division point therein according to the time sequence of the respective point. A (k+1)N.times.N (N is the number of strokes) matrix P consisting of the elements of row (k+1)(n-1)+1 and column k(m-1)+j comprising angle .OMEGA. formed by the straight line connecting division point i of stroke n and division point j of stroke m and the straight line connecting division point i of stroke n and division point j+1 of stroke m, is then calculated. The distance di between handwritten character Cx and matrix Qi (i=1-M) formed in the same manner from M templates corresponding to characters possessing the same number of strokes as character Cx, is calculated as the sum of the squares of each element of matrix P-Qi. The character Cz corresponding to the template Qz with the smallest distance dz is then selected as the recognition result.
    • 本发明提供了一种手写字符识别方法,其中相邻点通过直线连接,然后使用虚线插入中间点之间的片段。 每个虚线均等分成k个段,并且根据各个点的时间顺序向其中的每个分割点分配一个数字。 由行(k + 1)(n-1)+1和列k(m-1)+ j的元素组成的包括由OMEGA形成的角度OMEGA组成的矩阵P的矩阵P(A)(k + 1)NxN(N是笔画数) 然后计算连接行程n的划分点i和行程m的分割点j的直线以及连接行程n的划分点i和行程m的分割点j + 1的直线。 以对应于具有与字符Cx相同数量的笔画的字符的M个模板以相同的方式形成的手写字符Cx与矩阵Qi(i = 1-M)之间的距离di被计算为每个元​​素的每个元素的平方和 矩阵P-Qi。 然后选择与具有最小距离dz的模板Qz相对应的字符Cz作为识别结果。
    • 5. 发明授权
    • Rainfall, snowfall forecast apparatus and method
    • 降雨,降雪预报装置及方法
    • US5406481A
    • 1995-04-11
    • US266541
    • 1994-06-28
    • Kazuhiko ShinozawaNoboru SoneharaTadasu Uchiyama
    • Kazuhiko ShinozawaNoboru SoneharaTadasu Uchiyama
    • G01S13/95G01S15/88G01W1/10G06F15/54
    • G01W1/10G01S15/885G01S13/95Y10S706/931
    • An apparatus is presented for providing a short time range forecast with relative high accuracy from weather radar images of cloud reflection data by incorporating physical properties of cloud in the forecasting method. The method consists of defining a plurality of lattice points on a radar image, and multiplying the reflection data from a group of neighboring lattice points obtained at a specific past point in time with selected coefficients. The products of multiplication are summed, and transformed into image data by specific function based on the properties relating to cloud. Squared errors of the difference between the computational reflection data and the observed reflection data are iterated to a value below a predetermined threshold value to select the coefficients, and these coefficients are used to provide forecasting of reflection data at a specific future point in time.
    • 提出了一种通过在预测方法中结合云的物理属性来提供来自云反射数据的天气雷达图像的相对高精度的短时间范围预测的装置。 该方法包括在雷达图像上定义多个格点,并将来自在特定过去时间点获得的一组相邻格点的反射数据与所选择的系数相乘。 乘法乘积相加,并根据与云相关的属性通过特定函数转换为图像数据。 将计算反射数据和观察到的反射数据之间的差的平方误差迭代到低于预定阈值的值以选择系数,并且这些系数用于在特定的未来时间点提供反射数据的预测。
    • 9. 发明授权
    • Weather forecast apparatus and method based on recognition of echo
patterns of radar images
    • 基于识别雷达图像回波模式的天气预报装置和方法
    • US5796611A
    • 1998-08-18
    • US538723
    • 1995-10-03
    • Keihiro OchiaiHideto SuzukiNoboru Sonehara
    • Keihiro OchiaiHideto SuzukiNoboru Sonehara
    • G01S7/41G01S13/95G01W1/10G06F17/10
    • G01W1/10G01S13/95G01S7/417Y10S706/931
    • The present invention provides a weather forecast apparatus and a method for the same, to systematically classify a measured radar image based on results of pattern classification of past radar images so as to use the classified radar image. In the present invention, rapid forecasting is possible by making the FNN model previously learn based on data of each class (and indexes for forecast times) obtained by classification of past weather data for every resembling pattern. In addition, a calculation procedure for improving the classifying ability of patterns can be established by varying the procedure for calculating feature quantities with regard to the radar image by using the learning of the TNN model. Furthermore, systematic classification of a pre-learned image can be realized by performing self organization with regard to compound feature quantities extracted from a radar image in the PNN model, a typical example of which is a competitive learning model.
    • 本发明提供一种天气预报装置及其方法,基于过去的雷达图像的模式分类的结果对测量的雷达图像进行系统分类,以便使用分类的雷达图像。 在本发明中,通过使FNN模型先前基于通过对于每个类似模式的过去天气数据的分类获得的每个类别(和预测时间的索引)的数据来进行快速预测是可能的。 另外,可以通过使用TNN模型的学习改变用于计算关于雷达图像的特征量的过程来建立用于提高图案分类能力的计算过程。 此外,可以通过对PNN模型中的雷达图像提取的复合特征量进行自组织来实现预先学习的图像的系统分类,其典型的例子是竞争性学习模型。