ID 原文 译文
47996 针对意图规划过程的层次性和序列性,提出了序列贝叶斯网络(series Bayesian network,SBN)模型, For intent to the hierarchy of planning process and sequence, sequence Bayesian network (series Bayesian network, SBN) model,
47997 并将动态贝叶斯网络(dynamic Bayesian network,DBN)模型和SBN模型结合起来, and dynamic Bayesian networks (dynamic Bayesian network, DBN) model and the SBN model,
47998 构建了动态SBN(dynamic SBN,DSBN)模型进行规划识别, builds the dynamic SBN (dynamic SBN, DSBN) is used to identify the planning model,
47999 模型的DBN部分用于由特征序列推理元意图,SBN部分用于由序列意图逐层推理父意图。 the model of DBN to yuan intentions, by the characteristics of sequence reasoning SBN to attempt reasoning step by step a parent by sequence.
48000 推导了模型的算法,分析了模型在规划识别问题中的表达和推理能力。 Model of the algorithm was deduced, and analyzes the model expression and reasoning ability in planning identify problems.
48001 实验表明,DSBN模型能够有效根据特征序列识别战术意图。 Experiments show that the DSBN model can effectively identify tactical intention according to the characteristics of the sequence.
48002 基于机载海洋波谱仪挂飞试验获得的实测数据,研究了波谱仪的信号处理和波浪参数反演算法。 Based on the airborne spectrometer hang fly test ocean measured data, studies the signal processing and wave spectrometer parameters inversion algorithm.
48003 首先,采用海浪谱反演算法对挂飞试验获得的波谱仪数据进行处理,成功得到了海洋波浪方向谱,并反演出了波浪相关参数; First of all, the wave spectrum inversion algorithm is adopted to hang fly test spectrometer for data processing, a successful got the direction of ocean wave spectrum, and the performance related parameters with the waves.
48004 然后,利用浅海波浪模型Kadomtsev-Petviashivilli(KP)方程模拟了该海域的波浪传播,研究该海域波浪的传播特性。 Then, using the shallow water wave model Kadomtsev - Petviashivilli (KP) equation to simulate the spread of the sea waves, the propagation characteristics of the ocean waves.
48005 将波谱仪测量结果与模拟结果进行对比,结果基本一致,证明了波谱仪信号处理和波浪参数反演算法的有效性, The spectrometer to measure the results compared with the simulation results, the results are basically identical, proved that the spectrometer signal processing and wave parameters inversion algorithm is effective,