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
多源动态最优潮流的分布鲁棒优化方法(IEEE118节点)(Matlab代码实现)内容概要:本文介绍了基于Matlab代码实现的多源动态最优潮流的分布鲁棒优化方法,适用于IEEE118节点电力系统。该方法结合两阶段鲁棒模型与确定性模型,旨在应对电力系统中多源输入(如可再生能源)的不确定性,提升系统运行的安全性与经济性。文中详细阐述了分布鲁棒优化的建模思路,包括不确定性集合的构建、目标函数的设计以及约束条件的处理,并通过Matlab编程实现算法求解,提供了完整的仿真流程与结果分析。此外,文档还列举了大量相关电力系统优化研究案例,涵盖微电网调度、电动汽车集群并网、需求响应、储能配置等多个方向,展示了其在实际工程中的广泛应用价值。; 适合人群:具备一定电力系统基础知识和Matlab编程能力的研究生、科研人员及从事能源系统优化工作的工程师。; 使用场景及目标:①用于研究高比例可再生能源接入背景下电力系统的动态最优潮流问题;②支撑科研工作中对分布鲁棒优化模型的复现与改进;③为电力系统调度、规划及运行决策提供理论支持与仿真工具。; 阅读建议:建议读者结合提供的Matlab代码与IEEE118节点系统参数进行实操演练,深入理解分布鲁棒优化的建模逻辑与求解过程,同时可参考文中提及的其他优化案例拓展研究思路。
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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