How To Use a 32-Bit Application to Determine When a Shelled Process Ends

本文介绍了一个使用VBScript创建并管理外部进程的应用实例。通过定义STARTUPINFO和PROCESS_INFORMATION结构体,调用CreateProcessA函数来启动notepad.exe,并等待其结束。此示例展示了如何在VBScript中控制进程生命周期。
  Private Type STARTUPINFO
      cb As Long
      lpReserved As String
      lpDesktop As String
      lpTitle As String
      dwX As Long
      dwY As Long
      dwXSize As Long
      dwYSize As Long
      dwXCountChars As Long
      dwYCountChars As Long
      dwFillAttribute As Long
      dwFlags As Long
      wShowWindow As Integer
      cbReserved2 As Integer
      lpReserved2 As Long
      hStdInput As Long
      hStdOutput As Long
      hStdError As Long
   End Type

   Private Type PROCESS_INFORMATION
      hProcess As Long
      hThread As Long
      dwProcessID As Long
      dwThreadID As Long
   End Type

   Private Declare Function WaitForSingleObject Lib "kernel32" (ByVal _
      hHandle As Long, ByVal dwMilliseconds As Long) As Long

   Private Declare Function CreateProcessA Lib "kernel32" (ByVal _
      lpApplicationName As String, ByVal lpCommandLine As String, ByVal _
      lpProcessAttributes As Long, ByVal lpThreadAttributes As Long, _
      ByVal bInheritHandles As Long, ByVal dwCreationFlags As Long, _
      ByVal lpEnvironment As Long, ByVal lpCurrentDirectory As String, _
      lpStartupInfo As STARTUPINFO, lpProcessInformation As _
      PROCESS_INFORMATION) As Long

   Private Declare Function CloseHandle Lib "kernel32" _
      (ByVal hObject As Long) As Long

   Private Declare Function GetExitCodeProcess Lib "kernel32" _
      (ByVal hProcess As Long, lpExitCode As Long) As Long

   Private Const NORMAL_PRIORITY_CLASS = &H20&
   Private Const INFINITE = -1&

   Public Function ExecCmd(cmdline$)
      Dim proc As PROCESS_INFORMATION
      Dim start As STARTUPINFO

      ' Initialize the STARTUPINFO structure:
      start.cb = Len(start)

      ' Start the shelled application:
      ret& = CreateProcessA(vbNullString, cmdline$, 0&, 0&, 1&, _
         NORMAL_PRIORITY_CLASS, 0&, vbNullString, start, proc)

      ' Wait for the shelled application to finish:
         ret& = WaitForSingleObject(proc.hProcess, INFINITE)
         Call GetExitCodeProcess(proc.hProcess, ret&)
         Call CloseHandle(proc.hThread)
         Call CloseHandle(proc.hProcess)
         ExecCmd = ret&
   End Function

   Sub Form_Click()
      Dim retval As Long
      retval = ExecCmd("notepad.exe")
      MsgBox "Process Finished, Exit Code " & retval
   End Sub
先看效果: https://renmaiwang.cn/s/jkhfz Hue系列产品将具备高度的个性化定制能力,并且借助内置红、蓝、绿三原色LED的灯泡,能够混合生成1600万种不同色彩的灯光。 整个操作流程完全由安装于iPhone上的应用程序进行管理。 这一创新举措为智能照明控制领域带来了新的启示,国内相关领域的从业者也积极投身于相关研究。 鉴于Hue产品采用WiFi无线连接方式,而国内WiFi网络尚未全面覆盖,本研究选择应用更为普及的蓝牙技术,通过手机蓝牙与单片机进行数据交互,进而产生可调节占空比的PWM信号,以此来控制LED驱动电路,实现LED的调光功能以及DIY调色方案。 本文重点阐述了一种基于手机蓝牙通信的LED灯设计方案,该方案受到飞利浦Hue智能灯泡的启发,但考虑到国内WiFi网络的覆盖限制,故而选用更为通用的蓝牙技术。 以下为相关技术细节的详尽介绍:1. **智能照明控制系统**:智能照明控制系统允许用户借助手机应用程序实现远程控制照明设备,提供个性化的调光及色彩调整功能。 飞利浦Hue作为行业领先者,通过红、蓝、绿三原色LED的混合,能够呈现1600万种颜色,实现了全面的定制化体验。 2. **蓝牙通信技术**:蓝牙技术是一种低成本、短距离的无线传输方案,工作于2.4GHz ISM频段,具备即插即用和强抗干扰能力。 蓝牙协议栈由硬件层和软件层构成,提供通用访问Profile、服务发现应用Profile以及串口Profiles等丰富功能,确保不同设备间的良好互操作性。 3. **脉冲宽度调制调光**:脉冲宽度调制(PWM)是一种高效能的调光方式,通过调节脉冲宽度来控制LED的亮度。 当PWM频率超过200Hz时,人眼无法察觉明显的闪烁现象。 占空比指的...
In R, a permutation test can be used to determine an appropriate threshold. Here is a general example code to illustrate the process of using a permutation test to determine a threshold: ```R # Generate some sample data set.seed(123) group1 <- rnorm(50, mean = 0, sd = 1) group2 <- rnorm(50, mean = 1, sd = 1) data <- c(group1, group2) group <- factor(rep(c("Group1", "Group2"), each = 50)) # Calculate the observed test statistic observed_stat <- mean(group1) - mean(group2) # Number of permutations n_permutations <- 1000 permuted_stats <- numeric(n_permutations) # Perform permutation test for (i in 1:n_permutations) { permuted_group <- sample(group) permuted_stat <- mean(data[permuted_group == "Group1"]) - mean(data[permuted_group == "Group2"]) permuted_stats[i] <- permuted_stat } # Calculate the p - value p_value <- mean(abs(permuted_stats) >= abs(observed_stat)) # Determine the threshold based on a significance level (e.g., 0.05) significance_level <- 0.05 threshold <- quantile(abs(permuted_stats), 1 - significance_level) # Print the results cat("Observed test statistic:", observed_stat, "\n") cat("P - value:", p_value, "\n") cat("Threshold:", threshold, "\n") ``` In this code: 1. First, sample data is generated for two groups. 2. The observed test statistic (the difference in means between the two groups) is calculated. 3. A specified number of permutations are performed. In each permutation, the group labels are randomly shuffled, and the test statistic is recalculated. 4. The p - value is calculated based on the proportion of permuted statistics that are more extreme than the observed statistic. 5. The threshold is determined by taking the appropriate quantile of the absolute values of the permuted statistics based on the chosen significance level.
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