会员体验
专利管家(专利管理)
工作空间(专利管理)
风险监控(情报监控)
数据分析(专利分析)
侵权分析(诉讼无效)
联系我们
交流群
官方交流:
QQ群: 891211   
微信请扫码    >>>
现在联系顾问~
热词
    • 31. 发明授权
    • Method of saving power for video camera
    • 摄像机省电方法
    • US5710597A
    • 1998-01-20
    • US557897
    • 1995-11-14
    • Kenji TamakiKoichi Yahagi
    • Kenji TamakiKoichi Yahagi
    • H04N5/228H04N5/232H04N5/225
    • H04N5/232Y10S358/906
    • A video camera having a taking lens of multiple focal lengths is provided with a separate, multiple focal length view finder lens. When power has been supplied to the camera via the "on" switch but a switch for video recording has not yet been closed, the focal length of the view finder is automatically made to coincide with the focal length of the taking lens so as permit the scene to be viewed in the same frame size as it will appear when recording, but without wasting power by supplying power to those circuits of the taking lens that form electrical signals of the scene or that record the scene. In addition, in a preferred embodiment of the invention, six different power level consumption states are provided so as to supply power only to the minimum circuitry required for each state.
    • 具有多个焦距的拍摄镜头的摄像机设置有单独的多焦距取景器镜头。 当通过“开”开关向相机供电时,视频记录开关尚未闭合时,取景器的焦距自动与拍摄镜头的焦距一致,以便允许 在与记录时将出现的相同帧大小的情况下观看场景,但是通过向形成场景的电信号或记录场景的拍摄镜头的那些电路供电而不浪费电力。 此外,在本发明的优选实施例中,提供六个不同的功率电平消耗状态,以便仅向每个状态所需的最小电路供电。
    • 33. 发明授权
    • Monitoring diagnostic device and monitoring diagnostic method
    • 监测诊断装置和监测诊断方法
    • US09378183B2
    • 2016-06-28
    • US13699018
    • 2011-05-11
    • Kenji Tamaki
    • Kenji Tamaki
    • G01B5/28G06F17/00G05B23/02G01M13/00
    • G06F17/00G01M13/00G05B23/0224G05B23/0281
    • An abnormality monitoring process milt (2) divides sensor data collected from a monitoring-target apparatus (8) into sensor data for each of a plurality of condition modes based on a condition-mode transition point detected by a condition-mode transition point detecting process unit (21), and sorts the divided sensor data into a plurality of groups. Next, for each condition mode and each group, each piece of sensor data is compared with past statistic data, thereby detecting an abnormality. A causal diagnosis process unit (3) diagnoses an abnormality cause using link models before and after an abnormality is detected built based on a correlation coefficient between two pieces of sensor data in each group.
    • 异常监视处理milt(2)基于由条件模式转变点检测处理检测到的条件模式转变点,将从监视目标装置(8)收集的传感器数据分成用于多个条件模式中的每一个的传感器数据 单元(21),并将分割的传感器数据分割成多个组。 接下来,对于每个条件模式和每个组,将每个传感器数据与过去的统计数据进行比较,从而检测异常。 因果诊断处理单元(3)基于各组中的两个传感器数据之间的相关系数,构建检测到异常之前和之后的链路模型来诊断异常原因。
    • 34. 发明授权
    • Electric vehicle
    • 电动车
    • US08970061B2
    • 2015-03-03
    • US13395038
    • 2010-09-07
    • Hideaki NakagawaIsao ShokakuKenji Tamaki
    • Hideaki NakagawaIsao ShokakuKenji Tamaki
    • B60L1/00B60L3/04B60L3/00B60L11/18B62K11/10B60K1/04B60K7/00B60K11/06B60K1/00
    • B60L3/04B60K1/04B60K7/0007B60K11/06B60K2001/005B60K2007/0038B60K2007/0061B60L3/0046B60L11/1818B60L11/1868B60L11/1877B60L2200/12B60L2210/10B60L2210/30B60Y2200/126B62K11/10B62K2202/00B62K2204/00B62K2208/00Y02T10/7005Y02T10/7066Y02T10/7072Y02T10/7088Y02T10/7216Y02T10/7241Y02T10/92Y02T90/121Y02T90/127Y02T90/14
    • In an electric vehicle in which a high voltage battery supplying electric power to an electric motor generating power to drive a drive wheel and a low voltage battery supplying electric power to an accessory are mounted in a vehicle body, a breaker (62) is provided in a circuit (74) of a high power system linked to the high voltage battery (36); manual connection-disconnection means (71) for allowing switching between connection and disconnection of a circuit (75) of a low power system linked to the low voltage battery (40) to be performed by a manual operation is provided in the circuit (75) of the low power system; a relay switch (63, 64) which is capable of performing switching between connection and disconnection of the circuit (74) of the high power system by being supplied with electric power from the circuit (75) of the low power system, and which interrupts the circuit (74) of the high power system when the circuit (75) of the low power system is interrupted is provided on the circuit (74) of the high power system; and touch prevention means allows the breaker (62) to be touched only when the circuit (75) of the low power system is interrupted by using the manual connection-disconnection means (71). Accordingly, it is possible to make a work procedure in the maintenance of a breaker of a high power system easier to follow.
    • 在车辆中安装有向电动机供给电力以驱动驱动轮的高电压电池和向附件提供电力的低压电池的电动车辆中设置有断路器(62) 连接到高压电池(36)的高功率系统的电路(74); 在电路(75)中设置有用于允许在通过手动操作执行的连接到与低电压电池(40)连接的低功率系统的电路(75)的连接和断开之间进行切换的手动连接断开装置(71) 的低功率系统; 能够通过从低功率系统的电路(75)提供电力来进行大功率系统的电路(74)的连接和断开之间的切换的继电器开关(63,64),并且其中断 当高功率系统的电路(75)中断时,高功率系统的电路(74)设置在大功率系统的电路(74)上; 并且触摸防止装置允许仅当通过使用手动连接断开装置(71)中断低功率系统的电路(75)时才能触发断路器(62)。 因此,可以使维修高功率系统的断路器的工作程序更容易遵循。
    • 35. 发明授权
    • Apparatus abnormality diagnosis method and system
    • 仪器异常诊断方法及系统
    • US08676553B2
    • 2014-03-18
    • US13130059
    • 2009-11-17
    • Toshiharu MiwaKenji Tamaki
    • Toshiharu MiwaKenji Tamaki
    • G06F7/60G06G7/48G06F11/00
    • G05B23/0283G05B23/0278G06F11/3476
    • A technique relating to an apparatus abnormality diagnosis system, capable of easily creating and adding/updating an diagnosis model with respect to an initial and new failure case, and appropriately and efficiently achieving diagnosis of abnormality and instruction of operation using the model. In the abnormality diagnosis system, an diagnosis model creating process unit creates a structured abnormality model expressing a structured abnormality of maintenance operation type to an alarm and apparatus event relating to the maintenance operation type by a graph network structure based on acquisition of maintenance operation data. And, by synthesizing the structured abnormality model with an existing structured abnormality model, the diagnosis model is updated.
    • 一种与装置异常诊断系统相关的技术,能够容易地创建和添加/更新关于初始和新的故障情况的诊断模型,并且适当且有效地实现使用该模型的异常诊断和操作指示。 在异常诊断系统中,诊断模型生成处理部基于维护操作数据的获取,通过图形网络结构,创建表示维护操作类型的结构化异常的结构化异常模型与关于维护操作类型的报警和装置事件。 并且,通过利用现有的结构化异常模型合成结构化异常模型,更新诊断模型。
    • 36. 发明申请
    • PREDICTION SYSTEM AND PROGRAM FOR PARTS SHIPMENT QUANTITY
    • 部分运输量的预测系统和程序
    • US20130332233A1
    • 2013-12-12
    • US13981094
    • 2011-02-23
    • Naoko KishikawaJun TateishiKenji Tamaki
    • Naoko KishikawaJun TateishiKenji Tamaki
    • G06Q30/02
    • G06Q30/0202G06Q10/08
    • There is provided a system or others capable of predicting a parts shipment quantity varying in accordance with an assumed parts-purchase period (such as a charge-free warranty period of a product) (having a period length with taking a product shipping date as a starting point during which a customer is assumed to purchase parts) so that prediction accuracy can be increased more than a conventional one. In the prediction system (1) for the parts shipment quantity, a server (10) has a prediction unit (100) to which product data (D1), parts shipment data (D2), and a prediction condition (D3) containing information of the assumed parts-purchase period are inputted and which performs a process of predicting a future shipment quantity for each parts and of outputting prediction result data (D0). The prediction unit (100) performs a process of predicting the future shipment quantity for each parts in accordance with the assumed parts-purchase period.
    • 提供了一种系统或其他能够预测根据假定的零件购买期间(例如,产品的免费保修期)变化的零件出货量(具有将产品发货日期视为 假设客户购买零件的起点),以便可以比传统的预测精度提高预测精度。 在用于零件装运量的预测系统(1)中,服务器(10)具有产品数据(D1),零件装运数据(D2)和预测条件(D3)的预测单元(100) 输入假定的零件购买期间,并执行预测每个零件的未来出货量并输出预测结果数据(D0)的处理。 预测单元(100)根据假定的零件购买期间进行各零件的未来出货量的预测处理。
    • 37. 发明申请
    • APPARATUS ABNORMALITY MONITORING METHOD AND SYSTEM
    • 装置不正常监测方法与系统
    • US20120136629A1
    • 2012-05-31
    • US13377242
    • 2010-05-13
    • Kenji TamakiToshiharu Miwa
    • Kenji TamakiToshiharu Miwa
    • G06F15/00
    • G05B23/0254
    • The present invention relates to an apparatus abnormality monitoring method, and it provides a technology that can achieve accurate abnormality detection, cause diagnosis, and others. This system relates to monitoring of a newly-installed apparatus (T) among a plurality of similar apparatuses 1. In a judgment model creation module 2, for each of a plurality of (K) already-installed similar apparatuses (a, b and others), individual judgment models (prediction models) are created, and a meta prediction model for predicting a coefficient and an intercept of these prediction models from feature item values and others of each of the apparatuses 1 is created. From this meta prediction model, a prediction model dedicated to the apparatus (T) (judgment model including the prediction model) is produced. By using this judgment model, a judgment module 3T monitors the state of the apparatus (T) to perform abnormality detection.
    • 本发明涉及一种装置异常监测方法,提供可以实现精确的异常检测,导致诊断等技术。 该系统涉及多台相似设备1中的新安装设备(T)的监视。在判断模型生成模块2中,对于已经安装的多个(K)的类似设备(a,b等) ),创建个体判断模型(预测模型),并且创建用于从特征项值和每个装置1中的其他值预测这些预测模型的系数和截距的元预测模型。 从该元预测模型,生成专用于装置(T)的预测模型(包括预测模型的判断模型)。 通过使用该判定模型,判断模块3T监视装置(T)的状态进行异常检测。
    • 38. 发明授权
    • Quality control system for manufacturing industrial products
    • 制造工业产品质量控制体系
    • US07209846B2
    • 2007-04-24
    • US11171394
    • 2005-07-01
    • Kenji TamakiYouichi Nonaka
    • Kenji TamakiYouichi Nonaka
    • G06F19/00
    • G06Q10/06
    • In a quality control system for manufacturing industrial products, the product quality history and the manufacturing process history are collected and collated to calculate the correlation magnitude between the two histories. The candidates for the cause of quality variation hidden in the manufacturing processes are listed, and the correlation magnitude between all combinations of the variates of the manufacturing process history are calculated. Further, by utilizing the manufacturing sequence history used for an input plan, a causation connecting structure model between the manufacturing processes of the manufacturing line is automatically generated and automatically analyzed thereby to automatically extract the fundamental cause of quality variation from the candidates for the cause of quality variation. By doing so, the cause of quality variation of industrial products manufactured through a complicated process can be traced in a complicated connecting structure in the manufacturing history data.
    • 在制造工业产品的质量控制体系中,收集和整理产品质量历史和制造过程历史,以计算两个历史之间的相关幅度。 列出制造过程中隐藏的质量变化原因的候选者,并且计算制造过程历史的变化的所有组合之间的相关大小。 此外,通过利用用于输入计划的制造顺序历史,生产线的制造过程之间的因果连接结构模型被自动生成并被自动分析,从而自动地从考生的原因中提取质量变化的根本原因 质量变化。 通过这样做,通过复杂工艺制造的工业产品的质量变化的原因可以在制造历史数据中以复杂的连接结构来描绘。
    • 39. 发明申请
    • Quality control system for manufacturing industrial products
    • 制造工业产品质量控制体系
    • US20060047454A1
    • 2006-03-02
    • US11171394
    • 2005-07-01
    • Kenji TamakiYouichi Nonaka
    • Kenji TamakiYouichi Nonaka
    • G06F19/00
    • G06Q10/06
    • In a quality control system for manufacturing industrial products, the product quality history and the manufacturing process history are collected and collated to calculate the correlation magnitude between the two histories. The candidates for the cause of quality variation hidden in the manufacturing processes are listed, and the correlation magnitude between all combinations of the variates of the manufacturing process history are calculated. Further, by utilizing the manufacturing sequence history used for an input plan, a causation connecting structure model between the manufacturing processes of the manufacturing line is automatically generated and automatically analyzed thereby to automatically extract the fundamental cause of quality variation from the candidates for the cause of quality variation. By doing so, the cause of quality variation of industrial products manufactured through a complicated process can be traced in a complicated connecting structure in the manufacturing history data.
    • 在制造工业产品的质量控制体系中,收集和整理产品质量历史和制造过程历史,以计算两个历史之间的相关幅度。 列出制造过程中隐藏的质量变化原因的候选者,并且计算制造过程历史的变化的所有组合之间的相关大小。 此外,通过利用用于输入计划的制造顺序历史,生产线的制造过程之间的因果连接结构模型被自动生成并被自动分析,从而自动地从考生的原因中提取质量变化的根本原因 质量变化。 通过这样做,通过复杂工艺制造的工业产品的质量变化的原因可以在制造历史数据中以复杂的连接结构来描绘。