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随时间而变化的的英文

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"随时间而变化的"怎么读用"随时间而变化的"造句

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  • tempolabile

例句与用法

  • Both the inertance and capacitance effects are time-dependence .
    惯性和容量的效应都是随时间而变化的
  • A graph which shows this variation with time is called a waveform .
    电压或电流随时间而变化的图形称为波形。
  • Since the quantity of groundwater resources changes with time owing to the changes in land use , re - evaluation of groundwater resources based on the conditions changing with time is needed
    由于土地利用方式的改变,地下水资源是随时间而变化的,需要根据变化情况进行再评价。
  • Luminance meters . determination of metrological characteristics , classification according to the latter , calibration , check of the main characteristics liable to vary with time
    亮度计.计量特性测定.根据随时间而变化的主要特性的最近状况校准和检查结果测定计量特性并进行分类
  • This mechanical disturbance generates vibrations that can be represented as electrical signals by means of a device ( for example , a microphone ) , that converts these vibrations into a time - varying voltage
    这种干扰生成振动,借助某种能将振动转变为随时间而变化的电压的设备(比如麦克风) ,振动被以电子信号的方式呈现出来。
  • There are many complex systems in real life . in order to analyze , design and predict the system , we often want to model the dynamic systems of ordinary differential equations according to the observed data
    在现实生活中存在许多随时间而变化的复杂系统和现象,人们通常需要根据动态系统的观测数据建立合理的微分方程模型(动力学模型) ,为系统分析、设计和未来状态的预报提供依据。
  • For a long time , agricultural knowledge is so complicated that it ca n ' t be classified effectively , making knowledge - database redundant largely , data warehouse is an data aggregation which including knowledge decision - making , subject - orientation , integration , variation promptly and permanence
    长久以来,由于农业知识繁杂,不能进行有效的分类,造成知识库大量的冗余,数据仓库是一种面向主题的、集成的、随时间而变化的、持久的数据集合。
  • The second is use future threshold value probability . base on the markov optimization of the threshold value probability we introduce future threshold value changeable with time serving as a variable under the new state . the threshold value probability is maximized and the optimum strategy of markov is extracted
    第二种方法是基于未来阈值的方法,在马尔可夫阈值概率最优化的问题上,对于以马尔可夫转移的本来状态变量,引入随时间而变化的将来的阈值,并作为新的状态变量,在其新的马尔可夫转移的基础上,把所给予的阈值概率最小化,从中得到最优马尔可夫策略。
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