ABSTRACT Expecting the Unexpected Adaptation for Predictive Energy Conservation
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在MATLAB中遇到"syntax error, unexpected parameters" 的错误,通常表示MATLAB 遇到了无法识别或不符合语法规则的参数。
以下是一些可能的原因和解决方法:1. 拼写或语法错误:检查你的函数名、变量名和关键词是否拼写正确,以及是否符合MATLAB 的命名规则。
确保所有的括号、引号和逗号等符号都已正确配对和使用。
2. 参数数量不匹配:检查你调用函数时提供的参数数量是否与函数定义时的参数数量一致。
3. 数据类型不匹配:确保传递给函数的参数数据类型与函数期望的数据类型相匹配。
4. 未定义的函数或变量:确保你在调用的函数或变量在当前工作空间中已经定义。
5. 缺失的结束括号或分号:在MATLAB 中,语句通常以分号结尾,而函数或数组定义需要正确的开始和结束括号。
6. 意外的字符或符号:检查你的代码中是否有意外的特殊字符或者转义字符,这些可能会导致语法错误。
7. MATLAB版本兼容性问题:如果你的代码是在不同版本的MATLAB 上编写的,可能存在版本兼容性问题。
确保你的代码与当前MATLAB 版本的语法兼容。
要解决这个问题,你可以按照以下步骤进行:仔细检查报错行及其周围的代码,看看是否存在上述提到的问题。
使用MATLAB 的内置编辑器功能,如语法高亮和自动完成功能,可以帮助你发现潜在的错误。
分段调试你的代码,逐行运行或者使用dbstop if error 设置断点来定位错误发生的具体位置。
如果你仍然无法确定问题所在,尝试将出错的部分代码简化或者分段,以便更好地隔离和识别问题。
记住,MATLAB 的错误信息通常会提供关于错误发生位置和可能原因的详细信息,充分利用这些信息可以帮助你更快地找到并解决问题。
C#中abstract的⽤法详解abstract可以⽤来修饰类,⽅法,属性,索引器和时间,这⾥不包括字段. 使⽤abstrac修饰的类,该类只能作为其他类的基类,不能实例化,⽽且abstract修饰的成员在派⽣类中必须全部实现,不允许部分实现,否则编译异常. 如:using System;namespace ConsoleApplication8{class Program{static void Main(string[] args){BClass b = new BClass();b.m1();}}abstract class AClass{public abstract void m1();public abstract void m2();}class BClass : AClass{public override void m1(){throw new NotImplementedException();}//public override void m2()//{// throw new NotImplementedException();//}}}Abstract classes have the following features:抽象类拥有如下特征:1,抽象类不能被实例化, 但可以有实例构造函数, 类是否可以实例化取决于是否拥有实例化的权限 (对于抽象类的权限是abstract, 禁⽌实例化),即使不提供构造函数, 编译器也会提供默认构造函数;2,抽象类可以包含抽象⽅法和访问器;3,抽象类不能使⽤sealed修饰, sealed意为不能被继承;4,所有继承⾃抽象类的⾮抽象类必须实现所有的抽象成员,包括⽅法,属性,索引器,事件;abstract修饰的⽅法有如下特征:1,抽象⽅法即是虚拟⽅法(隐含);2,抽象⽅法只能在抽象类中声明;3,因为抽象⽅法只是声明, 不提供实现, 所以⽅法只以分号结束,没有⽅法体,即没有花括号部分;如public abstract void MyMethod();4,override修饰的覆盖⽅法提供实现,且只能作为⾮抽象类的成员;5,在抽象⽅法的声明上不能使⽤virtual或者是static修饰.即不能是静态的,⼜因为abstract已经是虚拟的,⽆需再⽤virtual强调.抽象属性尽管在⾏为上与抽象⽅法相似,但仍有有如下不同:1,不能在静态属性上应⽤abstract修饰符;2,抽象属性在⾮抽象的派⽣类中覆盖重写,使⽤override修饰符;抽象类与接⼝:1,抽象类必须提供所有接⼝成员的实现;2,继承接⼝的抽象类可以将接⼝的成员映射位抽象⽅法.如:interface I{void M();}abstract class C: I{public abstract void M();}抽象类实例:// abstract_keyword.cs// 抽象类using System;abstract class BaseClass // 抽象类{protected int _x = 100; //抽象类可以定义字段,但不可以是抽象字段,也没有这⼀说法.protected int _y = 150;public BaseClass(int i) //可以定义实例构造函数,仅供派⽣的⾮抽象类调⽤; 这⾥显式提供构造函数,编译器将不再提供默认构造函数. {fielda = i;}public BaseClass(){}private int fielda;public static int fieldsa = 0;public abstract void AbstractMethod(); // 抽象⽅法public abstract int X { get; } //抽象属性public abstract int Y { get; }public abstract string IdxString { get; set; } //抽象属性public abstract char this[int i] { get; } //抽象索引器}class DerivedClass : BaseClass{private string idxstring;private int fieldb;//如果基类中没有定义⽆参构造函数,但存在有参数的构造函数,//那么这⾥派⽣类得构造函数必须调⽤基类的有参数构造函数,否则编译出错public DerivedClass(int p): base(p) //这⾥的:base(p)可省略,因为基类定义了默认的⽆参构造函数{fieldb = p;}public override string IdxString //覆盖重新属性{get{return idxstring;}set{idxstring = value;}}public override char this[int i] //覆盖重写索引器{get { return IdxString[i]; }}public override void AbstractMethod(){_x++;_y++;}public override int X // 覆盖重写属性{get{return _x + 10;}}public override int Y // 覆盖重写属性{get{return _y + 10;}}static void Main(){DerivedClass o = new DerivedClass(1);o.AbstractMethod();Console.WriteLine("x = {0}, y = {1}", o.X, o.Y);}}以上所述是⼩编给⼤家介绍的C#中abstract的⽤法详解,希望对⼤家有所帮助,如果⼤家有任何疑问请给我留⾔,⼩编会及时回复⼤家的。
java abstractvariadicfunction函数-回复Java是一种面向对象的编程语言,具有许多强大的功能和特性,其中之一是它的抽象可变参数函数(Abstract Variadic Function)。
抽象可变参数函数是指可以接受不确定数量的参数并且在函数中以数组的形式进行处理。
在本文中,我们将逐步介绍如何在Java中创建和使用抽象可变参数函数。
首先,让我们来了解一下为什么抽象可变参数函数在Java中非常有用。
使用抽象可变参数函数,我们可以在不事先确定参数数量的情况下编写更加灵活和可扩展的代码。
这意味着我们可以在调用函数时传递任意数量的参数,并且函数可以自动将这些参数组合成一个数组来进行处理。
这对于处理不确定数量的输入非常方便,尤其是在我们需要编写通用的函数时。
为了创建一个抽象可变参数函数,我们需要使用省略号(Ellipsis)作为参数的类型,并给参数一个名称。
下面是一个示例:javapublic void abstractVariadicFunction(String... params) { 处理参数的代码}在这个示例中,我们创建了一个名为abstractVariadicFunction的函数,它接受一个名为params的可变参数。
params参数可以是任意数量的字符串参数,并且在函数中将以数组的形式进行处理。
现在,让我们来看看如何在函数内部使用这些可变参数。
由于params参数是一个数组,我们可以使用for循环或者其他数组操作来处理这些参数。
以下是一个示例:javapublic void abstractVariadicFunction(String... params) {for (String param : params) {System.out.println(param);}}在这个示例中,我们遍历params数组并打印出数组中的每个元素。
我们可以调用这个函数并传递任意数量的参数,所有的参数都将被打印出来。
there are no expected calls of the method 在软件开发中,经常会遇到一种情况,即在代码中存在未被使用到的方法。
这些方法被称为“没有被预期调用的方法”。
这个问题可能会让开发者感到无所适从,因为它可能会导致潜在的安全风险和额外的开销。
本文将介绍如何解决这个问题以及如何避免它的产生。
首先,我们需要理解为什么会出现没有被预期调用的方法。
通常情况下,这是由于代码的复杂性和变化性造成的。
在软件开发中,代码的变化是一个不可避免的过程。
可能会出现一些方法在过去被频繁使用,但现在已经不再使用了。
这些方法可能被遗留在代码中,但没有被删除。
另一种情况是,开发者可能在代码中添加了一些新的方法,但由于某些原因,这些方法没有被完全使用。
不管原因是什么,这些没有被预期调用的方法都需要被解决。
要解决这个问题,我们需要使用静态代码分析工具。
这些工具可以扫描代码库并检测未被使用的方法。
在发现这些方法后,我们需要决定是删除这些方法还是将它们标记为已弃用。
如果我们决定删除这些方法,需要确保它们不会影响到其他代码的正常运行。
另外,如果我们将这些方法标记为已弃用,则需要确保其他开发者不会再使用这些方法。
避免产生未被使用的方法的最好方法是在代码开发的早期进行代码审查。
在代码审查过程中,我们可以检查每个方法的用途以及是否需要被使用。
如果一个方法不再需要使用,应该被及时删除。
此外,我们还可以使用单元测试来确保代码的正确性。
通过编写单元测试,我们可以尽早发现代码中的问题,包括未被使用的方法。
总之,没有被预期调用的方法是一个很常见的问题,但也是可以解决的。
我们需要使用静态代码分析工具来检测这些方法,并决定是删除它们还是将它们标记为已弃用。
要避免这个问题的产生,我们应该在代码开发的早期进行代码审查,并使用单元测试来确保代码的正确性。
通过这些方法,我们可以提高代码的质量并减少潜在的安全风险。
FREEPASCAL编译时的出错信息Free pascal编译时的出错信息1.Out of memory[内存溢出]2.Identifier expected[缺标识符]3.Identifier not found[标识符未找到]*如:Identifier not found INTEGR[标识符INTEGER未找到] 4.Duplicate identifier[重复说明]*如:Duplicate identifier N[变量N重复说明]5.Syntax error[语法错误]*6.Error in real constant[实型常量错]7.Error in integer constant[整型常量错]8.String constant exceeds line[字符串常量跨行]9.Too many nested file[文件嵌套过多]10.Unexpected end of file[非正常文件结束]11.Line to long[行过长]12.Type Identifier expected[缺类型标识符]13.Too many open file[打开文件过多]14.Invalid file name[无效文件名]15.File not found[文件未找到]*16.Disk full[磁盘满]17.Invalid compiler directive[无效编译指示]18.Too many file[文件过多]19.Undefined type in pointer definition[指针定义中未定义类型]20.Variable identifier expected[缺变量标识符]21.Error in type definition[类型错误说明]*22.Stucture too large[结构过长]23.Set base type out of range[集合基类型越界]24.File components may not be files or object[FILE分量不能为文件或对象]25.Invalid string length[无效字符串长度]26.Type mismatch[类型不匹配]*27.Invalid subrange base type[无效子界基类型]28.Lower bound greater than upper bound[下界大于上界]29.Ordinal type expected[缺有序类型]30.Integer constant expected[缺整型常数]31.Constant expected[缺常量]32.Integer or real constant expected[缺整型或实型常量]33.Pointe type identifier expected[缺指针类型标识符]34.Invalid function result type[无效的函数结果类型]/doc/186557222.html,bel identifier expected[缺标号标识符]36.Begin expected[缺BEGIN]*37.End expected[缺END]*38.Integer expression expected[缺整型表达式]39.Ordinal expression expected[缺有序表达式]40.Boolean expression expected[缺布尔表达式]41.Operand type do not match operator[操作数与操作符不匹配]42.Error in expression[表达式错]43.Illegal expression[非法赋值]*44.Field identifier expected[缺域标识符]45.Object file too large[目标文件过大]46.Undefined external[未定义外部标识符]47.Invalid object file record[无效OBJ文件记录]48.Code segment too large[代码段过长]49.Data segment too large[数据段过长]*50.Do expected[缺DO]*51.Invalid PUBLIC definition[无效PUBLIC定义]52.Invalid EXTRN definition[无效EXTRN定义]53.Too many EXTRN definition[EXTRN定义过多]54.Of extected[缺0F]*55.INTERFACE expected[缺INTERFACE]56.Invalid relocatable reference[无效重定位引用]57.THEN expected[缺THEN]*58.TO(DOWNTO)expected[缺T0或DOWNTO]*59.Undefined forward[提前引用未定义的说明]60.Too many procedures[过程过多]61.Invalid typecast[无效类型转换]62.Division by zero[被零除]63.Invalid typecast[无效文件类型]64.Cannot Read or Write variable of this type[不能读写该类型的变量]*65.Ponter variable expected[缺指针变量]66.String variable expected[缺字符串变量]67.String expression expected[缺字符串表达式]68.Circular unit reference[单元循环引用]69.Unit name mismatchg[单元名不匹配]70.Unit version mismatch[单元版本不匹配]71.Duplicate unit name[单元重名]72.Unit file format error[单元文件格式错误]73.Implementation expected[缺IMPLEMENTATl0N]74.constant and case types do not match[常数与CASE类型不相匹配]75.Record variable expected[缺记录变量]76.Constant out of range[常量越界]77.File variable expected[缺文件变量]78.Pointer extression expected[缺指针变量]79.Integer or real expression expected[缺整型或实型表达式]/doc/186557222.html,ble not within currentblock[标号不在当前块中]/doc/186557222.html,ble already defined[标号已定义]82.Undefined lable in preceding statement part[在前面语句中标号未定义]83.Invalid@argument[无效的@参数]84.Unit expected[缺UNIT]85.“;”expected[缺“;”]*86.“:”expected[缺“:”]*87.“,”expected[缺“,”]*88.“(”expected[缺“(”)*89.“)”ex pected[缺“]”]*90.“=”expected[缺“=”]*91.“:=”expected[缺“:=”]*92.“[”or“(”expected[缺“[”或“(”)*93.“]”or“)”expected[缺“]”或“)”]*94.“..”expected[缺“.”]*95.“..”expected[缺“..”]*96.Too many variable[变量过多]97.Invalid FOR control variable[无效FOR控制变量]98.Integer variable expected[缺整型变量]99.File and procedure types are not allowed here[此处不允许用文件和过程类型] 100.Srting length mismatch[字符串长度不匹配] 101.Invalid ordering of fields[无效域顺序]102.String constant expected[缺字符串常量]103.Integer or real variable expected[缺整型或实型变量]104.Ordinal variable expected[缺顺序变量]105.INLINE error[INLINE错]106.Character expression expected[缺字符表达式]107.Too many relocation items[重定位项过多]112.Case constant out of range[CASE常量越界]113.Error in statement[语句错]114.Can’t call an interrupt procedute[不能调用中断过程]116.Must be in8087mode to complie this[必须在8087方式下编译]117.Target address not found[未找到目标地址]118.Include files are not allowed here[此处不允许包含INCLUDE文件]120.NIL expected[缺NIL]121.Invalid qualifier[无效限定符]122.Invalid variable reference[无效变量引用]123.Too many symbols[符号过多]124.Statement part too large[语句部分过长]126.Files must be var parameters[文件必须为变量参数]127.Too many conditional directive[条件符号过多]128.Misplaced conditional directive[条件指令错位]129.ENDIF directive missing[缺少ENDIF指令]130.Error in initial conditional defines[初始条件定义错]131.Header does not match previous definition[过程和函数头与前面定义的不匹配] 132.Critical disk error[严重磁盘错误] 133.Can’t evalute this expression[不能计算该表达式]*如:Can’t evalute constart expression[不能计算该常量表达式] 134.Expression incorrectly terminated[表达式错误结束]135.Invaild format specifier[无效格式说明符]136.Invalid indirect reference[无效间接引用]137.Structed variable are not allowed here[此处不允许结构变量]138.Can’t evalute without system unit[无SYSTEM单元不能计算]139.Can’t access this symbols[不能存取该符号]140.Invalid floating–point operation[无效浮点运算]141.Can’t compile overlays to memory[不能将覆盖模块编译至内存]142.Procedure or function variable expected[缺过程和函数变量]143.Invalid procedure or function reference.[无效过程或函数引用]144.Can’t overlay this unit[不能覆盖该单元]147.Object type expected[缺对象类型]148.Local object types are not allowed[不允许局部对象类型] 149.VIRTUAL expected[缺VIRTUAL]150.Method identifier expected[缺方法标识符]151.Virtual constructor are not allowed[不允许虚拟构造方法] 152.Constructor Identifier expected[缺构造函数标识符]153.Destructor Identifier expected[缺析构函数标识符]154.Fail only allowed within constructors[FAIL标准过程只允许在构造方法内使用] 155.Invalid combination of opcode and operands[无效的操作符和操作数组合] 156.Memory reference expected[缺内存引用]157.Can’t add or subtrace relocatable symbols[不能加减可重定位符号]158.Invalid register combination[无效寄存器组合]159.286/287Instructions are not enabled[未激活286/287指令]160.Invalid symbol reference[无效符号引用]161.Code generation error[代码生成错]162.ASM expected[缺ASM]。
abstractmethoderror案例1. 引言abstractmethoderror是Python中的一个常见错误类型,通常在使用抽象基类或继承的过程中发生。
在本文中,我们将深入探讨abstractmethoderror的定义、原因、解决方法,并通过案例分析来帮助读者更好地理解和避免这一错误。
2. 什么是abstractmethoderror?abstractmethoderror是指在Python中使用抽象基类(Abstract Base Classes, ABCs)或继承时,如果未实现抽象方法就会触发的错误。
在面向对象编程中,抽象基类是指包含抽象方法的类,这些方法必须在子类中被具体实现。
如果子类没有实现所有抽象方法,就会导致abstractmethoderror。
3. 导致abstractmethoderror的原因导致abstractmethoderror的原因通常有两种:3.1. 没有实现抽象方法:在子类中没有实现抽象基类中定义的抽象方法。
3.2. 调用未实现的抽象方法:在子类中调用了抽象基类中定义的但未被实现的抽象方法。
4. 解决abstractmethoderror的方法为了避免abstractmethoderror,我们可以采取以下方法来解决:4.1. 实现抽象方法:在子类中必须实现所有抽象基类中定义的抽象方法,确保不会出现未实现的情况。
4.2. 使用@abstractmethod装饰器:在抽象基类中使用@abstractmethod装饰器来标记抽象方法,以确保子类必须实现这些方法。
5. 案例分析让我们通过一个简单的案例来更好地理解abstractmethoderror。
假设我们有一个抽象基类Animal,其中定义了一个抽象方法speak(),我们再定义一个子类Dog,但在Dog类中忘记实现speak()方法。
当我们调用Dog类的speak()方法时,就会触发abstractmethoderror。
java语言的异常处理的主要子类JAVA语言的异常处理的主要子类:Error类子类名说明AbstractMethodError 调用抽象方法错误 ClassFormatError 类文件格式错误IllegalAccessError 非法访问错误 IncompatibleClassChangeError 非法改变一个类错误 InstantiationError 实例化一个接口或抽象类错误 IntemalError JAVA 内部错误 LinkageError 连接失败错误 NoClassDefFoundError 找不到类定义错误NoSuchFieldError 域未找到错误 NoSuchMethodError 调用不存在的方法错误OutOfMemoryError 内存溢出错误 StackOverflowError 堆栈溢出错误TheradDeadError 线程死亡错误 UnknownError 未知错误 UnstatisfideLinkError 链接不满足错误 VerifyError 校验失败错误 VirtualMachineError 虚拟机错误Exception类子类名说明 ClassNotFoundException 类未找到异常DataFormatException 数据格式异常 IllegalAceessException 非法存取异常InstantiationException 实例化异常 InterruptedException 中断异常NoSuchMethodException 调用不存在方法异常 RuntimeException 运行时异常RuntimeException类子类名说明 ArithmeticException 算术异常ArrayIndexOutOfBoundsException 数组越界异常 ArrayStoreException 数组存储异常 ClassCastException 类强制转换异常 IllegalArgumenException 非法参数异常 IllegalThreadStateException 非法线程状态异常IndexOutOfBoundException 索引越界异常 NumberFormatException 数值格式异常NegativeArraySizeException 负值数组大小异常 NullPointerException 空引用异常 SecurtyException 安全异常 StringIndexOutOfBoundsException 字符串越界异常下面是赠送的团队管理名言学习,不需要的朋友可以编辑删除!!!谢谢!!!1、沟通是管理的浓缩。
C++中abstract修饰类的用法1. 概述在C++中,我们经常会听到关于abstract类的概念。
那么,abstract 类到底是什么?它又有什么作用呢?2. 什么是abstract类在C++中,我们可以使用关键字“abstract”来修饰一个类,使其成为一个“abstract类”。
一个abstract类是一种不能被实例化的类,即不能创建它的对象。
abstract类通常用于定义接口和抽象的行为,它的目的是为了让其他类继承并实现它的纯虚函数。
3. abstract类的定义要定义一个abstract类,我们可以在类中声明纯虚函数。
纯虚函数是指在类中声明但没有实现的虚函数。
通过在函数声明后面加上“= 0”来将一个虚函数声明为纯虚函数。
例如:```C++class AbstractClass {public:virtual void pureVirtualFunction() = 0;};```4. abstract类的作用abstract类的作用主要有以下几点:- 定义接口:abstract类定义了一组接口,表示了一种抽象的行为。
其他类可以继承并实现这些接口。
这样一来,我们就可以通过基类指针来调用派生类的函数。
- 特定行为的约束:abstract类可以约束其派生类必须实现某些特定的行为。
这样一来,我们就可以确保派生类都具有相同的接口,从而提高代码的一致性和可维护性。
- 防止实例化:abstract类的对象不能被创建,这可以防止程序员错误地使用该类,从而避免一些潜在的错误。
5. 如何使用abstract类在C++中,我们可以通过继承abstract类并实现其中定义的纯虚函数来使用abstract类。
例如:```C++class ConcreteClass : public AbstractClass {public:void pureVirtualFunction() override {// 实现纯虚函数的具体逻辑}};```在上面的例子中,ConcreteClass继承了AbstractClass,并实现了其中定义的纯虚函数pureVirtualFunction。
must implement the inherited abstract method
当一个类继承自一个抽象类时,它必须实现该抽象类中的所有抽象方法。
如果在子类中没有实现父类中的抽象方法,编译器将会报出“must implement the inherited abstract method”错误。
例如,假设有一个抽象类Animal,其中有一个抽象方法eat。
现在我们创建一个子类Dog继承自Animal,但是没有实现eat方法,代码将会报错。
```
public abstract class Animal {
public abstract void eat();
}
public class Dog extends Animal {
// 缺少 eat 方法
}
```
在这种情况下,编译器将会报错“Dog must implement the inherited abstract method eat() from Animal”。
为了解决这个错误,我们需要在Dog类中实现eat方法。
```
public class Dog extends Animal {
@Override
public void eat() {
System.out.println('Dog is eating.');
}
}
```
这样就可以成功编译并运行程序了。
因此,一旦一个类继承自一个抽象类,就必须实现所有的抽象方法,否则编译器将会报错。
parse issue expected a type问题:[解析问题预期为一个类型],以中括号内的内容为主题,写一篇1500-2000字文章,一步一步回答回答:标题:解析问题- 为什么中括号内的内容需要一个类型?导言:在编程和计算机科学领域中,我们经常遇到各种类型的问题,这些问题需要我们理解问题的本质和背后的原理。
其中,解析问题是一种常见的问题类型,它要求我们分析并预测一段代码的执行结果或行为。
本文将以“解析问题”作为主题,深入探讨为什么我们在问题描述中需要指定一个类型,并通过一步一步的讲解来解释这个问题。
一、什么是解析问题解析问题是指要求我们根据给定的代码片段或程序,通过分析、推理和计算来预测其执行结果或行为的问题。
解析问题常见于编程语言、编译器和解释器的开发和使用过程中,也是计算机科学理论和实践中的一个重要环节。
二、解析问题中为什么需要指定一个类型在解析问题中,指定一个类型是为了准确描述问题的范围、内容和要求,让读者清楚需要注意的细节并能更好地理解问题。
对于解析问题,类型起到了以下几个方面的作用:1. 确定问题的限定条件:通过指定一个类型,可以明确问题的范围。
不同类型的解析问题可能涉及不同的编程语言、算法或数据结构,所以需要明确类型以确定问题讨论的范围。
2. 分析问题的关键点:指定一个类型可以帮助读者更准确地分析和理解问题的关键点。
在解析问题时,不同的问题类型可能会侧重于不同的细节或算法,指定一个类型可以让读者更加清晰地了解问题的核心。
3. 指导问题的解答过程:根据问题类型,我们可以有针对性地选择合适的方法和技巧来解答问题。
不同类型的解析问题可能需要使用不同的求解方法或工具,指定一个类型可以指导读者在解答问题时所采取的具体步骤。
三、解析问题中如何选择一个正确的类型为了选择一个正确的类型来描述解析问题,我们可以按照以下步骤进行:1. 仔细审题:在阅读和理解解析问题之前,我们需要仔细审题,了解问题要求和限定条件。
parse issue expected a type -回复以下是关于“[parse issue expected a type]”的主题文章,其中解释了何为“[parse issue expected a type]”并提供了一步一步的解决方法。
文章:解析问题:预期类型为“[parse issue expected a type]”错误及其解决方法简介:在编程过程中,我们常常会遇到各种各样的错误。
其中之一是“[parse issue expected a type]”错误。
这个错误提示通常会出现在代码中,表明编译器或解释器在特定位置期望一个类型(type),但发现了其他类型或缺少了必要的类型声明。
本文将一步一步地解释这个错误的含义,并提供了解决这个错误的方法。
第一步:理解错误信息当编译器或解释器遇到“[parse issue expected a type]”错误时,它意味着在代码的特定位置发现了一个无法匹配类型的问题。
这个位置通常是在变量、函数或表达式的声明处。
第二步:检查代码在出现这个错误之后,我们应该仔细检查相关的代码段。
首先,查看附近的语法,特别是变量和函数的声明,并确保它们已经按照正确的语法规则书写。
例如,在C语言中,变量声明应该遵循类型,然后是变量名的顺序。
第三步:检查类型匹配接下来,我们应该仔细检查变量、函数或表达式的类型是否与期望的类型匹配。
如果我们使用了错误的数据类型,编译器或解释器就会报错。
在检查类型匹配时,应该注意以下几点:1. 变量类型:确保所有变量在声明时都有正确的类型。
检查变量声明的位置,确保类型与期望的类型匹配。
2. 函数声明和调用:检查函数声明和调用之间的参数类型是否一致。
如果函数预期接受特定类型的参数,就必须传递该类型的参数。
3. 表达式类型:当我们进行数学运算、赋值或比较操作时,需要确保操作数的类型是兼容的。
例如,两个整数相加时,两个操作数的类型应该都是整数。
时滞取值概率未知下的线性时滞系统辨识方法刘 鑫1, 2摘 要 在大多数系统辨识方法中, 通常假设时变时滞在其可能的取值范围内服从均匀分布. 但是这种假设是非常受限的且在实际过程中常常无法得到满足. 因此在时滞取值概率条件未知的情况下, 针对一类线性时变时滞系统提出有效的辨识方法. 利用期望最大化(Expectation maximization, EM)算法将拟研究的辨识问题公式化, 期望最大化算法通过不断地迭代执行期望步骤和最大化步骤得到优化的参数估计. 在期望步骤中, 将未知的时变时滞当作隐含变量来处理并且假设可能的取值范围已知. 在每一个采样时刻, 时滞的变换由一个概率向量控制, 并且该向量中的每一个元素是未知的, 将其当作待估计的未知参数处理. 在算法的每次迭代过程中, 计算时滞的后验概率密度函数(Probability density function, PDF), 并在此基础上构造代价函数(Q-函数). 在最大化步骤中, 通过不断优化(Q-函数)来估计想要的参数, 包括模型参数、噪声参数、控制概率向量中的每一个元素和未知的时滞. 最后通过一个数值例子验证提出算法的有效性.关键词 系统辨识, 参数估计, 时变时滞, 时滞取值概率未知引用格式 刘鑫. 时滞取值概率未知下的线性时滞系统辨识方法. 自动化学报, 2023, 49(10): 2136−2144DOI 10.16383/j.aas.c201016Identification of Linear Time-delay Systems With UnknownDelay Distributions in Its Value RangeLIU Xin 1, 2Abstract In majority system identification methods, the varying system delay is generally assumed to be uni-formly distributed in its possible value range. However this assumption is very limited and it is often not satisfied in practical settings. Hence this paper addresses the identification of linear time-delay systems with unknown delay distributions in its value range. The formulation of the above identification problem is realized by using the expect-ation maximization (EM) algorithm which iteratively performs the expectation step (E-step) and the maximization step (M-step) until the desired optimal parameters obtained. In the E-step, the unknown varying time-delay is pro-cessed as the latent variable and its possible value range is assumed to be known as a priori. At each sampling in-stant, the variant of the time-delay is governed by a probability vector which is processed as the unknown paramet-er. In each iteration, the posterior probability density function (PDF) of the unknown delay is calculated which fa-cilitates the construction of the cost function (Q-function). In the M-step, the calculated Q-function is optimized in order to estimate the unknown parameters including the model parameters, the noise parameter, each element in the governmental probability vector and the unknown delay. Finally, the verification tests performed on a numeric-al example are provided to illustrate the effectiveness of the proposed algorithm.Key words System identification, parameter estimation, time-varying delay, unknown delay distributionsCitation Liu Xin. Identification of linear time-delay systems with unknown delay distributions in its value range.Acta Automatica Sinica , 2023, 49(10): 2136−2144系统辨识作为一种有效的系统建模手段, 在过去的一段时间里受到国内外学者的广泛关注[1−5]. 与传统的第一原理建模方法相比, 系统辨识方法直接通过可测的系统数据提炼出与实际系统等价的数学模型, 无需深入窥探系统的内部复杂机理, 因而使得建模过程变得简单、高效[6−11]. 在系统辨识领域,由于长时间的数据传输、数据获取方式的不同等因素, 常常导致不同的过程变量之间存在未知的时间延迟(时滞). 时滞作为一个常见的现实问题, 很容易降低辨识数据的数据质量并且对辨识算法的设计提出更高的要求[11−13]. 在辨识算法设计的过程中, 忽略时滞因素的影响会导致有偏的参数估计, 甚至错收稿日期 2020-12-08 录用日期 2021-03-02Manuscript received December 8, 2020; accepted March 2, 2021国家自然科学基金 (62103134), 江苏省自然科学基金(BK20200183)资助Supported by National Natural Science Foundation of China (62103134) and Natural Science Foundation of Jiangsu Province (BK20200183)本文责任编委 孙健Recommended by Associate Editor SUN Jian1. 河海大学物联网工程学院 常州 2130222. 中国矿业大学人工智能研究院 徐州 2211161. College of IoT Engineering, Hohai University, Changzhou 2130222. Artificial Intelligence Research Institute, China Uni-versity of Mining and Technology, Xuzhou 221116第 49 卷 第 10 期自 动 化 学 报Vol. 49, No. 102023 年 10 月ACTA AUTOMATICA SINICAOctober, 2023误的辨识结果[14−16].在实际的工业过程中, 过程变量之间的时滞大体上可以分为两类: 固定时滞和时变时滞[13−18]. 对于包含固定时滞的系统辨识问题, 传统的方法一般是分为两个步骤进行: 1) 对输入−输出数据利用相关分析法, 先求解未知的时滞参数; 2) 基于时滞参数求解未知的模型参数[17−21]. 但是这样的解决方案实质上是将时滞估计和参数估计独立分开执行, 很容易造成误差累计, 进而影响辨识结果的精度[17]. 为了解决误差累计的问题, 文献[22]在统计学的框架下并基于极大似然判据提出了一类有效的迭代辨识算法. 利用期望最大化(Expectation maximization, EM)算法首先将辨识问题公式化, 将未知的时滞变量当作隐含变量来处理. 在辨识过程中, 不断计算时滞的后验概率密度函数(Probability density function, PDF), 通过比较后验概率密度函数的值来判断未知时滞的取值. 该方法的主要优点包括: 1) 给出了显性的时滞估计公式; 2) 能够同时给出时滞参数和模型参数的极大似然估计, 避免了误差累计降低辨识结果的精度. 随后该辨识思想被重新利用在线性变参数时滞系统过程中, 文献[23]采用多模型方法对线性变参数时滞系统设计有效的局部辨识算法. 首先在每一个工作点处, 选取线性自回归时滞模型为局部模型, 每一个局部模型的时滞参数各不相同; 然后利用期望最大化算法同时估计各子模型的时滞参数、模型参数以及有效宽度参数;最后利用输出插值策略估计系统的全局输出. 在每一个采样时刻, 将系统全局输出写成每个子模型输出数据的加权组合的形式. 此外, 文献[24]结合了冗余法则以及递归最小二乘算法针对一类线性时滞系统进行了辨识研究. 核心思想是扩展原系统的维数, 然后再辨识扩展系统的模型参数. 如果某项系数趋近于零, 则认为该项在原系统中不存在, 并依据于此来判断时滞参数. 这种辨识思路无形中增加了算法的复杂度.针对时变时滞系统辨识而言, 首先要确定未知时滞的变化机制. 在现有的文献中, 最常用的变化机制是首先假定时滞可能的取值范围, 然后假定时滞在取值范围内服从均匀分布, 即在每个采样时刻,时滞取每个值的概率相同[3, 14, 16, 18−19]. 文献[25]针对多率采样时滞系统提出了一类有效的辨识方法. 输入、输出变量分别采用快率、慢率采样, 且输入和输出变量之间存在服从均匀分布的时变时滞. 利用期望最大化算法实现上述系统辨识问题, 将未知的时滞变量当作隐含变量来处理, 上述辨识问题可归纳为: 在慢率输出基础上同时估计时滞参数和模型参数. 另外, 基于辨识得到的有限脉冲响应模型还可以进一步促进输出丢失情况下的输出误差模型和传递函数模型的辨识问题. 文献[14]采用多模型局部辨识的思想针对线性变参数输出误差模型辨识进行深入研究. 能够同时给出各子模型的时滞参数和模型参数的估计公式, 由于目标代价函数与模型参数呈非线性关系, 采用阻尼牛顿法来迭代搜索模型参数的最优解. 此外, 文献[18]在输入输出数据双率采样的条件下考虑了线性状态空间时滞模型的辨识问题. 首先利用离散化技术得到待辨识系统的慢率采样模型; 利用数学工具将慢率采样模型转化成能观标准型; 利用卡尔曼滤波算法估计未知的状态变量; 最后利用随机梯度算法和递归最小二乘算法辨识模型参数. 在辨识过程中考虑了状态变量与输出变量之间存在未知的时变时滞, 且假定时滞在可能的取值范围内服从均匀分布.然而, 对于时变时滞系统而言, 认为时滞在可能的取值范围内服从均匀分布这个假设通常过于严格, 在很多情况下很难得到满足. 为了解决这个问题, 本文在时滞取值概率未知的条件下研究线性时滞系统辨识问题. 仍旧假定时滞可能的取值范围先验已知, 但是时滞取每个值的概率各不相同, 即在时滞的取值范围内, 时滞取每一个值的比重各不相同. 不难看出, 时滞服从均匀分布可以看作本文研究内容的一个特例, 因此本文的研究更加贴近实际应用. 采用极大似然算法搭建辨识框架, 将时滞当作隐含变量来处理. 在每个采样时刻不断计算时滞的后验概率密度函数, 且该函数作为权重自适应地分配给每一个数据点, 因此提高时滞的估计精度也能促进模型参数估计精度的提升. 同时给出模型参数、噪声参数、控制概率向量中的每一个元素和未知的时滞的估计公式, 最后利用一个数值例子验证算法的有效性.1 辨识问题阐述考虑以下线性双率采样时滞系统u1:N={u k}k=1,···,N∆t x ky T1:T M={y Ti}i=1,···,Mf∆t fτ1:M={τi}i=1,···,M e T ie Ti∼N(e Ti|0,r)G(z−1)其中, 表示快速率采样的输入数据且假设采样周期为; 表示理想的无噪声快率采样输出; 表示慢速率采样的真实输出数据, 它的采样周期假设为快率采样的整数倍, 即, 其中表示一个任意的正整数;表示未知的时变时滞; 表示输出测量噪声且服从零均值的高斯分布, 即. 表示系统的多项式传递函数并且有10 期刘鑫: 时滞取值概率未知下的线性时滞系统辨识方法2137m 其中, 表示系统的阶次. 结合式(1)和式(2), 系统的模型可改写为其中θ并且待辨识的模型参数 定义为此外, 系统输出量测过程则可改写为y T i 由式(6)不难看出服从以下高斯分布τi [1,J ][1,J ]在本文中, 将未知的时变时滞 当作隐含变量来处理. 首先假设时滞的取值范围已知, 即 . 在每个采样时刻, 假设时滞的取值为中的任意一个整数, 但取值概率不同且未知, 用数学语言描述如下:并且不难看出, 服从均匀分布的时变时滞系统可看作是本文的一个特例, 即C obs ={y T 1:T M ,u 1:N }C mis ={τ1:M }Θ={θ,β1:J ,r }C obs Θτ1:M 在本文中, 慢率采样的输出数据和快率采样的输入数据构成了可用辨识数据集 ; 利用未知的时变时滞构造隐含数据集 ; 待辨识的参数集构造为 .因此本文的主要任务可以进一步总结为: 基于可用数据集 设计有效的辨识算法, 同时估计参数集 和未知的时变时滞 .2 基于期望最大化(EM)算法的辨识算法推导2.1 EM 算法简介在系统辨识领域, EM 算法对于解决数据丢失问题或者隐含变量问题非常有效. EM算法是一个迭代优化算法, 通过不断重复执行期望步骤(E-step)和最大化步骤(M-step)以实现待辨识参数的估计.在E-step 中首先构造系统对数似然函数为由于隐含数据集不可用, 使得计算上述似然函数变得尤为困难. 这里通过计算其相对于隐含变量的期望来构造代价函数如下:Θs s 其中, 表示在第 次迭代中得到的参数估计值.在M-step 中, 通过优化目标代价函数以得到新的模型参数估计值L 1L 1重复执行E-step 和M-step, 直至参数收敛[26].期望最大化算法实质上是通过不断迭代优化 的条件期望函数来代替直接优化 函数, 最终得到所需的模型参数估计值. 算法1总结了EM 算法的具体实施步骤.算法 1. EM 算法Θs =Θ0s =0 1) 将待辨识的参数初始化 且设置 ;Q (Θ|Θs ) 2) E-step: 根据式 (12) 计算目标代价函数 ;3) M-step: 根据式 (13) 优化代价函数并得到新的参数估计值;s =s +1 4) , 转回到步骤 2), 直至参数收敛[26].2.2 基于EM 算法的辨识过程推导step1) E- 首先构造系统的对数似然函数如下:C 1=log p (u 1:N |Θ)y T i u 1:T i −1τi Θlog p (y T 1:T M |τ1:M ,u 1:N ,Θ)其中, 表示一个与参数无关的常数项. 基于式 (4)和式(6) 不难看出, 在每个采样时刻, 慢采样输出 与之前一段时间的输入变量 、未知时滞 以及模型参数 有关, 因此等式右边的概率密度函数 可进一步化简为再结合式 (7), 可以进一步化简为2138自 动 化 学 报49 卷τi log p (τ1:M |u 1:N ,Θ)此外, 由于时滞 与模型参数以及输入变量无关, 因此 可以进一步化简为结合式 (16)和式(17), 系统的对数似然函数最C 2=C 1−(M /2)log 2π其中, 是参数无关项. 此时再基于式 (12), 可求得τi 由于时滞变量 属于离散变量, 因此Q (Θ|Θ)p (τi =j |C obs ,Θs )观察式 (20) 可得, 想要优化代价函数 ,首先需要计算时滞取值的后验概率密度函数 . 根据文献[17]中给出的计算公式可得Q (Θ|Θs )为了方便优化, 可将代价函数 拆分成以下形式其中,2) M-stepQ 1(θ,r )θr θr =r s r θ=θs Q 1(θ,r )r 在此步骤中通过优化上述代价函数来得到参数估计. 首先 属于一个复合函数, 它同时是模型参数 以及噪声参数 的函数, 只能采取循环优化的方式得到优化的参数估计. 当优化 时, 令 为定值; 反之, 当优化 时, 令 为定值.将函数 对 求偏导并令导数等于零, 得θQ 1(θ,r )θQ 1(θ,r )由于模型参数 与代价函数 并非呈线性关系, 因此无法直接利用导数的方式求得的估计公式. 结合式 (3)、(4)和(23), 可将代价函数 改写为ϕT i −j 其中, 向量 定义为Q 1(θ,r )θQ 1(θ,r )此时, 函数 与模型参数 呈线性关系.再进一步对 求偏导可得Q 2(βj )βj 此外, 根据函数 求解 的迭代估计公式,10 期刘鑫: 时滞取值概率未知下的线性时滞系统辨识方法2139L β需要构造拉格朗日函数. 首先假设 为拉格朗日因子, 结合式 (9) 可构造所需的拉格朗日函数如下:通过求解上述拉格朗日函数可得并且在每个采样时刻, 未知时变时滞的迭代估计公式如下:θs r s θr 至此, 基于期望最大化算法的未知参数和未知时变时滞估计公式全部推导完成. 值得注意的是,在参数循环迭代的过程中, 只用到了前一次的模型参数 和噪声参数 . 因此在执行算法初始化时,只需设置 和 的初值. 本文提出的辨识算法可总结如算法2所示.算法 2. 时滞取值概率未知下的线性时滞系统辨识算法Θ0={θ0,r 0}s =0 1) 模型参数初始化 , 并且设定 ; 2) 根据式 (21) 计算每个采样时刻时滞可能取值的后验概率密度函数;3) 根据式 (22) ~ (24) 构造目标代价函数; 4) 根据式 (25) 计算噪声方差的迭代估计值; 5) 根据式 (28) 计算模型参数的估计值;6) 基于拉格朗日方程并且根据式 (30) 计算时滞取值概率的值;7) 根据式 (31) 不断估计未知的时变时滞;||Θs +1−ΘsΘs||2≤10−3s =s +1Θ∗={θ∗,β∗1:J ,r ∗} 8) 如果 , 结束此次循环, 否则 并且回到步骤 2), 最终得到的模型参数估计表示为 .3 辨识算法验证3.1 算法性能验证考虑以下双率采样的线性时滞系统u k =−1+2×N (0,1)f =5其中, 输入信号选取为幅值在 [−1, 1]之间的高斯白噪声, 即 . 输出数据采用慢率采样且采样周期是输入的5倍, 即 . 在每个慢率采样时刻, 时滞取值范围分布在[1, 4]中, 并且按照概率[0.35, 0.2, 0.25, 0.2]取值, 即r =0.01N =2000输出测量噪声选取为方差 的高斯噪声,且输出噪声与输入信号相互独立. 对于输入数据生成 个数据点, 则收集到的辨识数据如图1所示.−−采样时刻u−y图 1 输入输出辨识数据Fig. 1 The input-output identification dataθ在执行辨识算法之前, 首先需要将待辨识的参数初始化. 模型参数 的初始值选取为r 噪声方差 的初始值选取为然后执行算法2, 经过50次迭代之后, 得到的时滞分布向量估计值如下:同时, 得到模型参数的收敛曲线如图2所示.通过图2可以看出, 本文提出的辨识算法具有非常良好的收敛性, 经过少量的迭代之后, 每个模型参数都能精确地收敛到真实值附近. 此时, 未知的噪声参数迭代曲线如图3所示.此外, 结合式 (31), 得到未知时滞的估计结果如图4所示.2140自 动 化 学 报49 卷在图4中, 蓝色的实线表示真实的时滞; 红色的虚线表示估计的时滞. 经过统计, 时滞估计的正确率在91% 以上. 由此可见, 本文提出的辨识算法能够同时估计模型参数以及未知的时变时滞.θ0为了更加系统地验证本算法的有效性, 本文还设计了蒙特卡洛模拟仿真实验对算法进行更加系统的测试. 在接下来的测试中, 运行100次独立的蒙特卡洛仿真, 迭代次数设置为50次. 与文献[1]中类似, 每次独立的蒙特卡洛仿真的初始值都从以真实参数为中心的对称区间中选取未知的初值 . 得到的蒙特卡洛辨识结果如图 5 ~ 7所示.此外, 本文还在不同的噪声条件下测试了算法的性能. 首先定义信噪比 (Signal-to-noise rate, SNR)的计算式为−0.7−0.6−0.5−0.4−0.3−0.2迭代次数估计值 g 210203040500.50.60.70.80.91.0迭代次数估计值 g 310203040500.10.20.30.40.5迭代次数估计值 g 4(b)(c)(d)图 2 经过50次迭代得到的模型参数估计值Fig. 2 The estimated model parametersafter 50 iterations图 3 噪声方差的收敛曲线Fig. 3 The convergence curve of the identified noise variance时滞时滞采样时刻图 4 时滞的估计值与真实值对比Fig. 4 The comparison between the estimated and true delays10 期刘鑫: 时滞取值概率未知下的线性时滞系统辨识方法2141var (·)10dB 15dB 20dB 25dB 30dB 其中, 表示方差操作符. 分别在信噪比等于, , , , 时执行算法2,并且得到辨识结果, 如图8、图9和表1所示.从上述辨识结果可以得出以下结论:1) 从图2 ~ 4可以看出, 本文提出的辨识算法能够同时给出模型参数、噪声方差和时变时滞的估计值. 经过少数迭代之后, 模型参数和噪声参数可以精确地收敛到真实值; 同时时滞的估计值和真实值之间的匹配精度达到91%以上, 证明了所提算法的有效性.2) 从图5 ~ 7可以看出, 在蒙特卡洛仿真中,本文提出的辨识算法依然稳定, 在不同的初始值条件下得到的模型参数估计以及时滞参数估计都能精确地收敛到真实值, 这进一步证明了所提算法的稳定性. 对于迭代过程中出现的数值差异, 是由于参数初始值不同而造成的.3) 从图8、图9和表1可以看出, 当信噪比不断增大时, 意味着噪声不断减少且数据质量不断变好, 模型参数和时滞参数的估计精度不断提高, 并且随着数据质量不断提升, 模型参数收敛到真实值的速度也不断加快. 上述辨识结果也再次验证了所提算法的有效性.3.2 对比实验验证为了进一步验证算法2的有效性, 本文还设计了比较实验. 将算法2分别与两个基于递推最小二乘 (Recursive least squares, RLS) 辨识方法进行g 1g 2图 5 参数 和 的蒙特卡洛辨识结果g 1g 2Fig. 5 The identified and inMonte Carlo simulationsg 3g 4图 6 参数 和 的蒙特卡洛辨识结果g 3g 4Fig. 6 The identified and inMonte Carlo simulations图 7 噪声参数的蒙特卡洛辨识结果Fig. 7 The identified noise variance inMonte Carlo simulationsg 1g 2图 8 不同噪声下的 和 估计结果g 1g 2Fig. 8 The identified and underdifferent noise levels2142自 动 化 学 报49 卷20dB 比较. 在第1种方法中, 假设时滞参数精确已知, 将这种算法命名为 RLS-accurate; 在第2种方法中,忽略未知的时滞, 将这种算法命名为 RLS-ignore.输出量测噪声采用信噪比为 的高斯白噪声,通过执行上述三种辨识算法得到的结果如表2所示.表 2 对比实验结果Table 2 The identification results ofthe comparison tests参数真实值RLS-accurate算法2RLS-ignore g 1 1.5 1.4982 1.4861 1.1553g 2 −0.7−0.7025−0.6969−0.4136g 3 1.0 1.0092 1.01460.8348g 40.50.50520.49410.5256由上述辨识结果可以看出:1) 本文提出的辨识算法在参数估计精度上与RLS-accurate 方法相当. 再结合表1的辨识结果可以进一步看出, 本文提出的辨识算法在估计时滞参数的同时, 也能够精确地给出模型参数的估计结果.2) 由RLS-ignore 方法的辨识结果可以看出,如果忽略时滞因素的影响会造成有偏的参数估计,这从侧面证明了本文提出的算法2的有效性.4 结束语针对时滞取值概率未知的情况, 本文基于期望最大化算法提出了一类有效的线性时滞系统辨识算法. 将时滞的取值概率当成未知的参数来处理, 在辨识过程中所提出的算法能够有效地同时估计模型参数、噪声参数和时滞参数. 仿真结果表明, 本文提出的辨识算法具有快速的收敛速度以及较强的稳定性. 基于本文的研究内容, 未来的研究工作可注重以下两方面:1) 跳过高斯假设, 在更加复杂的数据条件下,例如在非高斯噪声、自相关噪声以及量化的输出条件下研究时滞系统辨识方法;2) 进一步考虑相邻时刻时滞间的相关性, 对于包含相关时滞 (例如: 马尔科夫时滞) 的系统辨识问题进行深入研究. 此方向的研究甚至可以扩展到马尔科夫切换系统辨识方法的研究.ReferencesHuang Yu-Long, Zhang Yong-Gang, Li Ning, Zhao Lin. 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Abstract封装,继承,多态封装:把⼀些数据(字段,属性) 和⾏为(⽅法) 包裹在⼀个单元---Class⾥;对外屏蔽细节(private...);对内公开⼀些属性和⽅法,具体是怎么做的不需要告诉⽤户。
访问修饰符:public 公开访问protected ⼦类访问internal 当前类库访问protected internal ⼦类且是当前类库访问private 私有访问继承:通过继承,⼦类可以拥有⽗类⼀切的属性和⽅法,任何⽗类出现的地⽅都可以⽤⼦类代替。
英⽂原话其实说的是:The Liskov Substitution Principle :(LSP, lsp) is a concept in Object Oriented Programming that states: Functions that use pointers or references to base classes must be able to use objects of derived classes without knowing it.使⽤任何⽗类指针或者引⽤的地⽅都可以⽤⼦类来替换⽽不⽤知道⼦类是什么。
还可以增加⼀些⽗类没有的属性和⽅法。
好处:代码的重⽤多态:多态是⾯向对象的重要特性,简单点说:“⼀个接⼝,多种实现”,就是同⼀种事物表现出的多种形态。
⾮虚⽅法的调⽤,由编译时决定虚⽅法的调⽤,由运⾏时决定ParentClass instance = new ChildClass();monMethod(); 普通⽅法的调⽤(⾮虚⽅法),调⽤⽗类的,由⽗类指针决定。
instance.VirtualMethod(); 虚⽅法的调⽤,调⽤⼦类的,运⾏时决定。
instance.AbstractMethod(); 抽象⽅法的调⽤,⽗类未实现,调⽤⼦类的。
1using MyAbstract.Abstract;2using System;3using System.Collections.Generic;4using System.Linq;5using System.Text;6using System.Threading.Tasks;78namespace MyAbstract9 {10///<summary>11///封装12///</summary>13public class People14 {15public int Id { get; set; }16public string Name { get; set; }17protected int Salary { get; set; }1819///<summary>20///呼吸:其实是⾝体多个部位协同⼯作的21///</summary>22public void Breath()23 {24 Console.WriteLine("Breath");25 }2627// 呼吸28//public29//protected ⼦类访问30//internal 当前类库访问31//protected internal32//private33 }34///<summary>35///学⽣36///</summary>37public class Student : People38 {39public new void Breath()40 {41 Console.WriteLine("Breath");42 }4344private int Tall { get; set; }4546public void Study()47 {48//base.Salary49 Console.WriteLine("学习.Net⾼级开发");50 }51public void PlayLumia(Lumia phone)52 {53 Console.WriteLine("This is {0} play {1}", , phone.GetType());54 phone.Call();55 phone.Photo();56 }57public void PlayiPhone(iPhone phone)58 {59 Console.WriteLine("This is {0} play {1}", , phone.GetType());60 phone.Call();61 phone.Photo();62 }6364public void PlayiPhone(Oppo phone)65 {66 Console.WriteLine("This is {0} play {1}", , phone.GetType());67 phone.Call();68 phone.Photo();69 }7071///<summary>72///⾯向基类73///</summary>74///<param name="phone"></param>75public void PlayPhone(AbstractPhone phone)76 {77 Console.WriteLine("This is {0} play {1}", , phone.GetType());78 phone.Call();79 phone.Photo();80 }8182public void PlayPhone<T>(T t) where T : AbstractPhone//基类83 {84 Console.WriteLine("This is {0} play {1}", , t.GetType());85 t.Call();86 t.Photo();87 }8889 }9091///<summary>92///⽼师93///</summary>94public class Teacher : People95 {96private int Tall { get; set; }9798public void Teach()99 {100 Console.WriteLine("教授.Net⾼级开发");101 }102103public void PlayiPhone(iPhone phone)104 {105 Console.WriteLine("This is {0} play {1}", , phone.GetType());106 phone.Call();107 phone.Photo();108 }109 }110 }View Code-----------------------------------------------------⽅法类型:void,int...普通⽅法----⼤家都是⼀样的virtual 虚⽅法(可以⽤override重写)---⼤家都有,但个别⼦类不⼀样,需要重写。
抽象类英语作文开头模板Abstract art, with its myriad forms and interpretations, stands as a testament to the boundless creativity of the human mind. Its allure lies in its ability to transcend conventional boundaries, inviting viewers into a realmwhere imagination reigns supreme. As we embark on a journey to explore the essence of abstract art, we are confronted with a myriad of questions and possibilities. What is the significance of abstract art in today's society? How doesit challenge our perceptions and evoke emotions that words often fail to express? In this essay, we delve into the depths of abstract art, unraveling its mysteries and uncovering the profound impact it has on both artists and audiences alike.At its core, abstract art defies the constraints of representation, eschewing recognizable forms and figures in favor of pure expression. It embodies a departure from the tangible world, instead embracing the intangible realms of emotion, thought, and sensation. In the absence of concretereferences, abstract art compels viewers to confront their own interpretations, sparking a dialogue between the artwork and the observer. This inherent ambiguity givesrise to a sense of liberation, as individuals are free to imbue the artwork with personal meaning, drawing from their unique experiences and perspectives.One of the defining characteristics of abstract art is its emphasis on form, color, and texture as vehicles for communication. Through bold strokes, dynamic compositions, and vibrant hues, artists convey a myriad of emotions and ideas, transcending the limitations of language andcultural barriers. In doing so, abstract art serves as a universal language, capable of resonating with individuals from diverse backgrounds and walks of life. Whether through the frenetic energy of Jackson Pollock's drip paintings or the serene harmony of Wassily Kandinsky's geometric abstractions, abstract art speaks to the depths of the human psyche, stirring the soul and igniting the imagination.Moreover, abstract art has the power to challengepreconceived notions and expand the boundaries of creativity. By pushing the limits of traditional techniques and conventions, artists forge new pathways of expression, pushing the envelope of what is possible in the realm of visual arts. This spirit of innovation is evident in the diverse array of movements within abstract art, from the geometric precision of Constructivism to the spontaneous improvisation of Abstract Expressionism. Each movement represents a departure from the status quo, inviting viewers to embrace change and embrace new ways of seeing the world.In addition to its aesthetic appeal, abstract art also holds profound philosophical implications, inviting contemplation on the nature of reality and perception. By stripping away the veneer of representation, abstract art lays bare the essence of existence, challenging viewers to confront the fundamental mysteries of the universe. In this sense, abstract art serves as a mirror, reflecting back to us our own innermost thoughts, fears, and desires. It invites us to venture into the unknown, to explore the depths of our subconscious minds, and to confront theinherent chaos and beauty of life itself.In conclusion, abstract art occupies a unique and indispensable place in the pantheon of human creativity.Its ability to transcend boundaries, challenge perceptions, and evoke profound emotions speaks to the enduring power of the artistic imagination. As we gaze upon the vibrant canvases and sculptural forms of abstract art, we are reminded of the limitless potential of the human spirit, forever reaching towards new horizons of expression and understanding. In a world fraught with uncertainty and turmoil, abstract art serves as a beacon of hope, illuminating the path towards a more enlightened and empathetic society.。
转--parsingerror:expected)解决⽅法VC6当我在界⾯上使⽤classwizard时,会弹出 Parsing error对话框,提⽰:Parsing error:Expected ") " inputline:“DDX_Check(pDX,IDC_CHECK0,m_bOutColumn[0]);”CSDN 解决,谢谢!问题解决!如下所⽰,⾃⼰⼿⼯添加的映射函数不要写到注释⾏中间。
添加到后⾯即可//{{AFX_DATA_INIT(CAssessweightPage)// NOTE: the ClassWizard will add member initialization here//}}AFX_DATA_INITDDX_Text(pDX, IDC_EDIT1, aifa[0]);DDX_Text(pDX, IDC_EDIT2, aifa[1]);DDX_Text(pDX, IDC_EDIT3, aifa[2]);DDX_Text(pDX, IDC_EDIT4, aifa[3]);DDX_Text(pDX, IDC_EDIT5, aifa[4]);MFC中有⼀种特殊的注释,叫注释宏。
注释宏⼀般由VC⾃动加⼊到你的代码中。
它是为class wizard服务的,class wizard通过它来定位各种系统⾃动添加代码的添加位置。
若要使⽤类向导添加成员变量和成员函数,则要保留注释宏;否则,必须⼿动添加。
如果你把它删了,classwizad就不能⾃动⽣成代码了。
你添加消息响应的时候是不是发现源代码⾥多了些代码??那些代码为什么会在那⾥出现?为什么不在别的⽂件⾥出现?就是因为那⾥有注释宏它要将代码⽣成在相应注释宏之间。
(这个注释是让ClassWizard能够分辨出哪些代码是它⽣成的,哪些是你⾃⼰写的。
你⾃⼰写的代码要在这个注释之外,这样ClassWizard再修改消息映射的时候就不会管你的代码了。
abstract关键字用法
abstract关键字用法有以下几种:
1. 抽象类的声明:在类的声明前加上abstract,表示该类是抽象类,不能直接实例化,只能被继承。
2. 抽象方法的声明:在方法的声明前加上abstract,表示该方法是抽象方法,没有具体的实现,需要子类去实现。
抽象方法没有方法体,用分号代替。
3. 抽象属性的声明:在成员变量的声明前加上abstract,表示该成员变量是抽象属性,没有具体的实现,需要子类去实现。
4. 接口的声明:接口中的所有方法默认都是抽象方法,所有成员变量默认都是public、static、final类型,可以不用声明为abstract。
5. 抽象类的继承:一个非抽象类可以继承一个抽象类,但必须实现该抽象类的所有抽象方法。
总的来说,abstract关键字的作用是定义抽象类、抽象方法或抽象属性,通过继承和实现来完成具体的操作。
expecting member declaratio “expecting member declaration”是一个编程错误,通常出现在C++或其他类似的面向对象的编程语言中。
这个错误意味着编译器在类的定义中期望找到一个成员声明,但是没有找到。
这通常是因为在类的主体中写了一些不应该出现的东西,或者忘记了在类的主体中写任何东西。
这里有一些可能导致“expecting member declaration”错误的常见原因:语法错误:在类的主体中,你可能不小心写了一些不符合语法的代码。
例如,你可能忘记了在成员函数的声明后面加上分号。
缺少成员声明:你可能只定义了一个空的类主体,而没有在里面声明任何成员。
虽然这在语法上是允许的,但通常不是一个好的做法,因为它创建了一个没有实际用途的类。
错误的成员声明:你可能试图在类的主体中声明一些不应该出现的东西,比如另一个类的定义或者一个全局变量的声明。
为了解决这个问题,你需要检查你的类定义,并确保所有的成员声明都是正确的,并且符合语法规则。
如果你不确定如何修复这个错误,你可以查阅相关的编程文档,或者在网上搜索类似的错误信息以找到解决方案。
这里有一个简单的C++类定义的例子,它应该不会导致“expecting member declaration”错误:cppclass MyClass {public:int myMemberVariable; // 成员变量声明void myMemberFunction(); // 成员函数声明};// 成员函数的定义可以在类的外部进行void MyClass::myMemberFunction() {// 这里是函数的实现}在这个例子中,MyClass 类有一个成员变量 myMemberVariable 和一个成员函数 myMemberFunction。
注意在类的主体中,成员函数的实现是被省略的,通常会在类的外部进行定义和实现。
abstractinterceptor使用详解
AbstractInterceptor是一个抽象类,作为Interceptor接口的默认
实现。
它提供了一些基本的方法和属性,方便开发人员实现自定义的拦截器。
使用AbstractInterceptor,你只需要继承这个类并实现相应的
方法即可。
AbstractInterceptor有以下几个重要的方法:
1. preHandle:在请求处理之前被调用。
可以通过返回一个布
尔值来决定是否打断后续拦截器和处理器的执行。
2. postHandle:在请求处理之后,渲染视图之前被调用。
可以
对ModelAndView进行修改。
3. afterCompletion:在请求处理完成之后被调用。
允许进行资
源清理等工作。
注意,这些方法都有一个入参是HandlerExecutionChain对象,它可以获取到当前请求的处理器和拦截器。
AbstractInterceptor还有一些其他方法和属性,例如:
1. getOrder:获取当前拦截器的执行顺序。
2. setOrder:设置当前拦截器的执行顺序。
3. excludePatterns:设置一个或多个URL的匹配模式,用于排除特定的请求。
4. includePatterns:设置一个或多个URL的匹配模式,用于包含特定的请求。
使用AbstractInterceptor可以方便地实现拦截器,并把它们添加到拦截器链中,对请求进行处理。
mashup exception expression error -回复Mashup Exception Expression Error: The Rise of Hybrid Art and its ImpactIntroduction:The emergence of the digital age has revolutionized the way we create and consume art. With the advent of technology, artists have gained access to a vast array of tools and techniques that were previously unavailable. One such innovation is the concept of mashup exception expression error, which has redefined the boundaries of traditional art forms. In this article, we will explore the meaning, evolution, and impact of mashup exception expression error in the art world.What is Mashup Exception Expression Error?Mashup exception expression error refers to a creative process where artists combine disparate elements from different sources to create new and unique works of art. It incorporates various mediums, such as music, visuals, and text, to create a hybridized form of artistic expression. This approach encourages therecontextualization and reinterpretation of existing material, resulting in fresh and innovative artistic creations. By blurring the lines between traditional art forms, mashup exception expression error challenges the notion of originality and encourages collaboration.Evolution of Mashup Exception Expression Error:The roots of mashup exception expression error can be traced back to the early 20th century, with the emergence of Dada and Surrealist movements. Artists like Marcel Duchamp and Salvador Dali challenged conventional notions of art by incorporating everyday objects and juxtaposing unexpected elements. However, it was with the rise of digital technology and the internet that mashup exception expression error truly came into its own. The accessibility of online content and the increasing use of software tools allowed artists to manipulate and combine existing material with unprecedented ease.Impact on the Art World:1. Breaking Down Barriers: Mashup exception expression error hasdemocratized art by breaking down traditional barriers to creativity. Artists no longer have to rely on expensive materials or formal training to create meaningful works. This has resulted in a more diverse and inclusive art scene, giving voice to marginalized communities and underrepresented artists.2. Redefining Copyright and Intellectual Property: The practice of mashup exception expression error has prompted a reassessment of copyright and intellectual property laws. While some argue that it is a form of plagiarism, others believe it is a legitimate form of artistic expression. Society is still grappling with finding the right balance between protecting intellectual property rights and fostering a creative environment.3. Encouraging Collaboration and Remix Culture: Mashup exception expression error thrives on collaboration and the exchange of ideas. Artists often borrow from each other's work, remixing and reimagining existing material. This collaborative approach encourages a culture of sharing and innovation, leading to the creation of new artistic movements and styles.4. Pushing Boundaries and Creating New Aesthetics: Mashupexception expression error challenges traditional notions of aesthetic beauty by combining contrasting elements. It pushes boundaries and encourages viewers to question their preconceived notions of art. This has resulted in the emergence of new aesthetics that challenge the status quo and open up new possibilities for artistic expression.Conclusion:Mashup exception expression error has revolutionized the art world by blurring the lines between traditional forms and embracing the digital age. By combining disparate elements, artists are able to create new and unique works that challenge conventional notions of art. While it has faced criticism for its perceived lack of originality and potential copyright issues, mashup exception expression error has undeniably had a profound impact on the art world. It has democratized access to creativity, redefined copyright laws, encouraged collaboration, and pushed artistic boundaries. As technology continues to evolve, it will be fascinating to witness how mashup exception expression errorevolves and shapes the future of art.。
Expecting the Unexpected: Adaptation for Predictive Energy Conservation Jeffrey P.Rybczynski,Darrell D.E.Long†Ahmed Amer‡Storage Systems Research Center Department of Computer Science University of California,Santa Cruz University of PittsburghABSTRACTThe use of access predictors to improve storage device per-formance has been investigated for both improving access times,as well as a means of reducing energy consumed by the disk.Such predictors also offer us an opportunity to demonstrate the benefits of an adaptive approach to han-dling unexpected workloads,whether they are the result of natural variation or deliberate attempts to generate a prob-lematic workload.Such workloads can pose a threat to sys-tem availability if they result in the excessive consumption of potentially limited resources such as energy.We pro-pose that actively reshaping a disk access workload,using a dynamically self-adjusting access predictor,allows for con-sistently good performance in the face of varying workloads. Specifically,we describe how our Best Shifting prefetching policy,by adapting to the needs of the currently observed workload,can use15%to35%less energy than traditional disk spin-down strategies and5%to10%less energy than the use of afixed prefetching policy.Categories and Subject DescriptorsD.4.3[Operating Systems]:File Systems Management;D.4.5[Operating Systems]:Reliability;D.4.6[Operating Systems]:Security and Protection;H.3.m[Information Storage and Retrieval]:Miscellaneous—Caching,Prefetch-ing,Power Management,StorageGeneral TermsAlgorithms,Design,Security,PerformanceKeywordsMobile Computing,Power Management,Adaptive Policies, Prediction,Prefetching,Disk Spin-down†Supported in part by the National Science Foundation un-der award CCR-0204358‡Supported in part by the National Science Foundation un-der award ANI-0325353.Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on thefirst page.To copy otherwise,to republish,to post on servers or to redistribute to lists,requires prior specific permission and/or a fee.StorageSS’05,November11,2005,Fairfax,Virginia,USA.Copyright2005ACM1-59593-223-X/05/0011...$5.00.1.INTRODUCTIONIn addition to protecting storage and data from intru-sion and misuse,ensuring the security of a storage device includes guaranteeing the system’s continued availability in the face of differing workloads.Unforeseen workloads can be the result of unexpected changes in load or application behavior,or even the result of malicious and deliberate user activity.In the latter case,it may be possible to limit re-quests from problematic client systems or applications,but even with a nominally light load a workload can be harmful to the system.For example,two clients making the exact same number of requests to a disk subsystem can result in radically different energy consumption by the system.To see this,consider a set of read requests that are presented to a disk in one burst.After these requests are satisfied,and a timeout period of inactivity has passed,the disk may en-ter a low-power state to conserve energy and remain in that state until the next set of requests arrives.On the other hand,if the exact same number of requests arrive with an inter-arrival time that is slightly greater than the inactivity timeout of the disk,it will continuously enter a low power state,only to be returned to an active state almost immedi-ately afterwards.This behavior leads to the consumption of excess energy at each such awakening,the disk spin-up cost expended to bring the disk back to active state,resulting in a great deal of excess energy being expended.As long as afixed spin-down policy is employed,the system is vulner-able to encountering such unexpected problem workloads, whether they be deliberately or naturally pathological. One method to avoid such problematic workloads is to develop fully adaptive strategies for managing the disk sub-system.By continually and dynamically adapting the disk spin-down and prefetching policies in response to the cur-rent workload,such strategies provide a system that utilizes the best policy available in the face of the current workload, regardless of how such a workload may vary.In this case we look at the problem of conserving disk energy consumption, but in doing so we are also effectively minimizing total disk motion and reducing unnecessary spin-ups and spin-downs. This in turn implies a reduction in overall mechanical wear and an increase in the reliability and availability of the disk and the system as a whole.In this manner,a policy aimed at reducing disk energy consumption and activity can miti-gate the effects of malicious and pathological workloads,as well as increasing the overall longevity of hard drives. While processors are still the main consumer of system power,it has been shown that the hard disk can use up to 30%of the total system energy[9],making the disk sub-system a prime candidate for energy conservation.Much research has been dedicated to conserving energy,particu-larly in mobile environments.We use prediction and its ap-plication to energy conservation as a means to illustrate the benefits of adaptive and self-optimizing strategies in the face of varying workloads.We contend that even in applications where prediction is the goal,such adaptive management is advantageous in handling the inevitably unpredictable and variant.2.PREDICTORS AND DISK POWERDisk systems use a significant amount of energy.Unlike most electronics in a computer,the disk has mechanical com-ponents.The spinning disk platters and the actuator arm require a considerable amount of energy to start operation. Powering down the disk to conserve energy is therefore only worthwhile if the disk can remain idle long enough to con-serve as much as the additional energy that would be needed to spin the disk back up again.Aside from intelligently de-ciding whether to spin the disk down for each idle period (dynamic disk spin-down),another technique is to actively reshape the workload by prefetching data that will be re-quested in the future,which would result in longer periods of inactivity between such bursts.Different access prediction policies can meet with varying success for different work-loads,and it is interesting to note that the most accurate predictors are not necessarily the most beneficial for conserv-ing energy.Our Best Shifting policy provides an effective energy-conserving predictor,while automatically updating its prediction policy in light of the current workload.2.1Accurate Prediction is Not OptimalTo allow our disk spin-down policy the longest idle peri-ods,a perfect predictor will need to both prefetch read data, as well as judiciously delay or accelerate write-backs of mod-ified data back to the disk.To test our dynamic policy,and competing predictors,we evaluate the performance of such a perfect oracle for each test workload.Simply prefetching the next N items(even if you are per-fectly accurate)is not always the best strategy.Assume that we have an access pattern which contains items A,B,C and D,and we are trying to create long disk idle periods so we can spin the disk down.Now,assume the following access sequence:ABABCDDDBBBB(shown in Figure1)and that we have a cache with a capacity of2items.If we assume that all items are predictable,then predicting the next items thatfit in the cache will get us an access pattern as shown in Figure1(b),with three idle periods of length three.As we can see from the sequence,if we fetch D and prefetch B towards the end of the trace(i.e.,delaying the eviction of D),then we can reduce this to only two idle periods,one of length three and the other of length six(Figure1(c)). Through dynamic programming we identify the behavior of a perfect oracle,rather than relying on simply prefetching the next N items which,as was shown in Figure1(b),is not an optimal strategy.The oracle works on the principal that all accesses are in one of three categories,either they are predictable,un-predictable,or may be delayed.Predictable accesses are for data that has been requested before,and assuming a prefetching algorithm was smart enough,could be predicted and prefetched.An unpredictable disk access,whether it is a write that needs to happen immediately or just afile that has not been requested before,refers to accesses that cannot be predicted and will always result in disk activity.Accesses that can be delayed are a special case.These are accesses which can be postponed for at most a given time period.Af-ter that time period expires,if the data has yet to be written to disk or read,it becomes equivalent to an unpredictable disk access that must happen immediately.An example of an access that can be postponed is a write request stored in a write buffer and waiting for aflexible time-out before be-ing written to disk.This allows the system to batch writes with other requests based on the current state of the disk so that they can all go to the disk at the same time,creating a busier burst period and a possibly longer idle period.Weis-sel et al.[18]have also shown thatflexible write time-outs can be used to batch disk requests and save disk power. The oracle is also optimal in terms of the spin-down policy. If the idle period is long enough to make a spin-down effi-cient,then this perfect oracle assumes a spin-down occurs at the start of the idle period.This allows the oracle to repre-sent not only optimal prefetching and request batching,but also optimal spin down strategy as well.The energy value estimated for our oracle is therefore a strict lower bound on how much disk energy a given trace would require.2.2Best-Shifting PredictionTo demonstrate the effectiveness and feasibility of dynam-ically adapting a predictive disk power management mech-anism to the current workload,we present the Best Shifting policy.Since we will demonstrate how different prefetching policies can perform best for different workloads,and that access patterns can vary substantially,it would be best to use an algorithm that would automatically switch to the best prefetching policy in response to the current workload.This is the basis of our Best Shifting prefetching policy,which ad-justs which prefetching algorithm it uses based on which is likely to conserve the most energy for the current access pattern.The Best Shifting policy uses machine learning techniques to choose which policy out of six implemented component algorithms works best for the workload at that point in time. The Best Shifting policy dynamically chooses the best pol-icy,not based upon hit ratio performance,but rather,based on dynamically estimated energy savings for each compo-nent policy.The six component predictors we evaluate in comparison to our Best Shifting policy,are:Unmodified,Last Succes-sor[1,2],First Successor[1,2],Stability[1,2],FMOC[11], and EPCM[12].Each of these prefetching policies use past access events to predict future accesses.The unmodified policy simply leaves the original workload unchanged,while the Successor and Stability predictors are based on simple pair-wise associations.The FMOC and EPCM predictors are based on data compression and context modeling.To keep track of the performance of the different pre-dictors,each policy has its own virtual cache,containing the data it would have in the cache if it were the system’s prefetching policy.The virtual cache then allows us to derive which data accesses would cause disk activity for the differ-ent policies.Each policy then stores these disk accesses in its own Disk Access Window,which is a snapshot of all the disk accesses a given policy would have created in the past N sec-onds had it been the system’s prefetching policy.From this window,we can directly estimate potential energy consump-A B C D B AB AC BD CB D123456789101112idleidleidle (a)No prefetching AB CD BE B AD CE B123456789101112idle idle idle (b)Perfect greedy prefetchingAB CD B AD CB D123456789101112idle idleDB (c)Optimal PrefetchingFigure 1:The initial request pattern with even spaces between each request and the corresponding disk accesses for:no prefetching,greedy prefetching,and optimal prefetching.tion.Virtual caches were used previously by Ari et al.[3]and Gramercy et al.[8].Our use of virtual caches differs in that we evaluate each policy’s performance based on the es-timated energy cost of using that policy,and not the simple hit ratios that it would have achieved.Best Shifting uses the virtual caches to determine the disk accesses each policy would have created had it been the system’s policy.Then,it periodically calculates the idle durations and estimates the energy used by each policy.The policy with the lowest esti-mated energy usage is adopted as the policy for the cache.This selection is actually based on a relative weighting of the components,and the use of a machine-learning algorithm to dynamically adjust these weights.When Best Shifting ’s policy changes,there are two strate-gies that we can employ to realize this change.First we can simply change the policy without affecting the contents of the cache.This changes the way future predicted data will be prefetched,though it does nothing to the data already in the cache.The second strategy is to “roll over”the cache.This is the process of synchronizing the virtual cache of the winning policy with the actual cache.This operation,how-ever,can take many disk accesses to perform,and so is only to be attempted if the disk is in the active state.If the cache policy changes while the disk is down,roll-over does not take place.If the disk is already active,then we can fetch the data we need to synchronize the cache in the background without causing an unnecessary disk spin-up.Roll-over can quickly help cache performance after the policy is switched due to a workload change that favors the new policy.3.EXPERIMENTAL RESULTSTo test Best Shifting ,our dynamic prefetching policy,we used file system traces from a varied selection of sources.A cache emulator is used to model the system cache while keeping track of the demand-fetched and prefetched files,along with cache statistics.If a trace entry asks for a filethat is not in the cache,a disk request is created.Our cache emulator records the timing of file accesses that result in cache misses and require physical disk activity.This output is then used as the input to a spin-down algorithm,and disk energy usage can then be calculated.For our tests,we used a cache emulator with a typical 30second write-buffer time-out.The output for this emulator was then run through a dynamic spin-down algorithm,implemented as described by Helmbold et al.[10].The difference among policies was the prefetching algo-rithms,which were used to predict and prefetch possible future data requests.The prefetched files are then placed in the system cache,alongside normal demand fetched files.The cache then uses LRU to decide which files should be evicted.Thus prefetching incorrect files can adversely affect the performance of the cache and reduce the length of idle periods.That is why it is important to use prefetching poli-cies that are accurate and effective.We have implemented six different prefetching policies.Each also uses an under-lying LRU cache eviction algorithm while predicting and prefetching files.Different prefetching policies can be seen to work better for different workloads,and even for the same workload ob-served over different days.Figures 3(a)and 3(b)show how different prefetching algorithms work better when using day long traces from instructional computers at the University of California,Berkeley [16].Figure 2shows the difference in performance for workloads observed on a Windows PC at the University of California,Santa Cruz during early 2005.The energy usage presented in these figures is depicted as a ratio of the estimated energy used by the given policy against the ideal energy usage of the oracle,which has the benefits of perfect prescience and perfect (instant and in-fallibly judicious)spin-down decisions.Both sets of traces demonstrate that a single prefetching policy does not al-ways perform best.This is typical of practically all tracesFigure2:The energy usage for host Periodot at the Uni-versity of California,Santa Cruz on February14,2005.(a)Day1(b)Day2Figure3:The energy usage for host INS#23at the University of California,Berekely on October3and4, 1996.we have observed,and suggests that it would be best to use management algorithms which automatically switch to the best policy in response to the current workload.The Best Shifting policy,which aims to do just that,can be seen to consistently offer the best performance compared against all otherfixed and non-adaptive policies.4.RELATED RESEARCHEffectively spinning down the disk drive can save valuable energy in a mobile environment[13,7].We have used a dy-namic spin-down algorithm which adjust the timeout value based on past disk request history.Helmbold et al.[10]de-scribed another dynamic spin-down timeout algorithm that employed a machine learning algorithm to adjust the time-out.Bisson and Brandt demonstrated the practicality of implementing such an algorithm[4].One of the earliest proposals for the incorporation of prediction to dynamically save energy in storage systems was offered by Wilkes[19]. The nature of the access workload,and its interaction with the underlying cache and disk,is crucial for for effective disk power management.Zhu et al.[20]showed that simply min-imizing cache misses does not necessarily result in the min-imum energy usage for a given cache replacement policy. They proposed four different power-aware caching policies that can save up to16%disk energy over a traditional LRU cache policy.Creating busier burst periods and longer idle periods allows the disk to be spun down for longer periods of time.The exploitation and promotion of such bursty be-havior has been explicitly attempted by Weisel et al.[18], and Papathanasiou and Scott[15].They found that tra-ditional OS resource management policies tend to“smooth out”these burst and idle periods.The Milly Watt Project[5] contended that because application needs are the driving force behind power management strategies,it is useful to propagate energy efficiency information to the application. Nobel’s implementation,Odyssey[14],showed a factor of five increase in performance over three different benchmarks. More specifically,Flinn et al.[6]showed that collaboration between the operation system and applications can be used to achieve longer battery life and less energy consumption. Their implementation in the Linux kernel monitored energy supply and demand to select a tradeoffbetween energy con-servation and performance.Others have also used dynamic collaboration between the operating system and applications to increase energy efficiency and performance[18,17]. 5.CONCLUSION&FUTURE RESEARCH Increasing system longevity,and increasing the overall re-liability a system is an important goal when providing secure and available storage.Resources can be wasted,and avail-ability threatened,by unforeseen changes in workload,as well as potentially deliberate problem workloads.One as-pect of increasing availability in the face of such a threat is workload reshaping with the goal of reducing disk en-ergy consumption.By creating increasingly bursty disk ac-cess patterns and longer disk idle periods through prediction and prefetching,such active workload reshaping can increase disk energy savings.But while some predictors are better than others,there is rarely a universal single choice of al-gorithm that is best for all possible workloads.This is par-ticularly true if the nature of workload change is a result of deliberate attempts to produce a problematic,though light,workload.In such a situation,a dynamically self-optimizing algorithm,such as our Best Shifting prefetcher,has the po-tential to leverage the best strategy regardless of the encoun-tered request patterns.This particular prefetcher,when combined with a dynamic spin-down mechanism,results in the use of15%to35%less energy than traditional predictive prefetching and spin-down policies.Here we have focused solely on prefetchers and disk en-ergy,but we aim to investigate the benefits of dynamic adap-tation for other subsystems,and in the face of more deliber-ate adversaries.While we have shown that by dymanically selecting the prefetching strategy we can lengthen disk idle periods and save energy,our oracle results also show that there is possibly more energy yet to be saved.By imple-menting different prefetching strategies,with the goal not only to make more accurate predictions but also to create busier burst periods,we aim to come closer to optimal disk energy savings,regardless of the workload our algorithms may encounter.6.ACKNOWLEDGEMENTSWe are grateful to all the members of the Storage Sys-tems Research Center who are a constant source of stim-ulating comments,especially Prof.David Helmbold and to our sponsors(Engenio,Hewlett-Packard Laboratories,Hi-tachi Global Storage Technologies,IBM Research,Intel Re-search,Microsoft Research,Network Appliance and Veri-tas)for theirfinancial support.We are also grateful to our colleagues at the University of Pittsburgh’s Department of Computer Science,especially members of the Storage Re-search Group,and particularly Prof.Panos K.Chrysanthis, of the Advanced Data Management Technologies Labora-tory,for valuable discussions.Jeffrey Rybczynski wishes to express his special thanks to Prof.Patrick Mantey for his support during the author’s graduate studies,and for his insightful comments on this research.7.REFERENCES[1]Amer,A.,and Long,D.D.E.Noah:Low-costfileaccess prediction through pairs.In Proceedings of the20th IEEE International Performance,Computing and Communications Conference(IPCCC’01)(Apr.2001), IEEE,pp.27–33.[2]Amer,A.,Long,D.D.E.,Pˆa ris,J.-F.,and Burns,R.C.File access prediction with adjustable accuracy.In Proceedings of the International PerformanceConference on Computers and Communication(IPCCC ’02)(Phoenix,Apr.2002),IEEE.[3]Ari,I.,Amer,A.,Gramacy,R.,Miller,E.L.,Brandt,S.A.,and Long,D.D.E.ACME:adaptive caching using multiple experts.In Proceedings inInformatics(2002),vol.14,Carleton Scientific,pp.143–158.[4]Bisson,T.,and Brandt,S.A.Adaptive diskspin-down algorithms in practice.In Proceedings of the 2004Conference on File and Storage Technologies(FAST)(2004).[5]Ellis,C.S.The case for higher-level powermanagement.In HOTOS’99:Proceedings of theSeventh Workshop on Hot Topics in Operating Systems (Washington,DC,USA,1999),IEEE ComputerSociety,p.162.[6]Flinn,J.,and Satyanarayanan,M.Energy-awareadaptation for mobile applications.In Symposium onOperating Systems Principles(1999),pp.48–63.[7]Golding,R.,Bosch,P.,Staelin,C.,Sullivan,T.,and Wilkes,J.Idleness is not sloth.In Proceedings of the Winter1995USENIX Technical Conference(New Orleans,LA,Jan.1995),USENIX,pp.201–212.[8]Gramacy,R.B.,Warmuth,M.K.,Brandt,S.A.,and Ari,I.Adaptive caching by refetching.InAdvances in Neural Information Processing Systems15 (2003),MIT Press,pp.1465–1472.[9]Greenawalt,P.M.Modeling power management forhard disks.In Proceedings of the2nd InternationalSymposium on Modeling,Analysis,and Simulation of Computer and Telecommunication Systems(MASCOTS ’94)(Durham,NC,Jan.1994),IEEE,pp.62–66. 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