步骤7：在求值的最后一步，向量中剩余的符号被结合到字符串，并在屏幕上显示。
Step 7: in the last step of the evaluation, the remaining tokens from vector are joined in a string and displayed on the screen.
将向量中剩余的符号结合到字符串并在屏幕上显示结果。
Join remaining tokens in the vector into the string and display the result on the screen.
显示单独的色彩向量。在选定区域显示为彩色时，图象以灰度显示。
"Solo" colour vector visualization. The image is shown in grey scale, while the selected ranges are shown in colour.
你可以猜的值存储在一个向量。然后遍历它来显示结果。
You could store the guessed values in a Vector. And then loop through it to show the results.
结果显示，海潮对GPS测站有厘米级的影响，而对基线向量和天顶延迟的测定的影响大约为几毫米。
The result shows that the effects are about several centimeters for GPS stations and several millimeters for baseline vectors and the determination of zenith delay in maximum.
实验结果显示，使用右十二经脉原穴配合支援向量机的线性核心是所有实验中准确率最高者，达到78.33%。
The lab result shows the high accuracy, 78.33%, came from the combination of right source-yuan points and linear core of support vector machine.
使用加密数据填充文件并在对话框窗口中显示相应的初始化向量是有意义的。
It makes sense to fill the file with the encrypted data and show the corresponding initialize vector in a dialog window.
实验结果显示，采用模糊支持向量机有效地提高了识别准确度。
The experiments show using fuzzy support vector machine significantly improves the overall recognition rate.
平面向量头戴式显示图标集。
Flat vector head-mounted displays icon set. Virtual and augmented reality gadgets.
为进行斜齿锥齿轮副的齿面接触分析，并直观显示齿面接触区，利用圆向量函数和球向量函数的矢量回转和坐标变换方法，建立了斜齿锥齿轮齿面接触分析的数学模型；
To analyze and display the contact of skew bevel involute gears, a mathematic model was established by vector gyration and coordinate transform of circle vector functions and sphere vector functions.
计算量比较结果显示，频域抽取多维向量基FFT算法比多维分离式FFT算法计算量低。
The comparison results show that, compared with multi-dimensional separable FFT, the DIF multi-dimensional vector radix FFT algorithm has lower calculation load.
结果显示，把最小二乘支持向量机回归预测与等步长时序预测相结合的预测方法应用于地下工程围岩位移监测数据的分析及预测是可行的；
Combining the advantages of regression analysis methods and time series forecast model with equal step length, a compound forecasting model was set up , and was tested with engineering data.
对结果均方差的分析显示，加权支持向量机的预测精度优于人工神经网络和标准支持向量机模型。
The analysis to the mean square deviation showed us the conclusion, that the prediction accuracy of WSVM was better than the ANN and traditional SVM models.
对结果均方差的分析显示，加权支持向量机的预测精度优于人工神经网络和标准支持向量机模型。
The analysis to the mean square deviation showed us the conclusion, that the prediction accuracy of WSVM was better than the ANN and traditional SVM models.
应用推荐