WASP [问题点数:40分]

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WASP水质模拟软件
WASP6是 USEPA 的一个改进了的Windows版本水质分析模拟程序(WASP) 。建模者对WASP6的开发完成有助于WASP的应用。 WASP6 包括一个前处理器, 一个快速数据处理器和一个图形处理器,这使得其必先前的WASP运行更快而且更便捷地以数字与图像评估模拟结果。在WASP6中, 模型的运行能被比早先的USEPA DOS操作系统界面快十倍。当然,WASP6采用与DOS版本相同的算法解决水质问题。
wasp风资源分析软件
<em>wasp</em>是一款风资源分析专业软件,此版本仅供学习用,不得作为其他用途。
WASP使用手册
Water Quality Analysis Simulation Program (WASP)是在1983年Di Toro等人建立模型的基础上的加强版。 优点:灵活性:能够模拟大部分水体类型,河流、湖泊、河口、海洋水体。 内部链接:热模块计算结果提供给富营养化模块,再用于有毒物质模拟。 外部链接:能够和多种模型耦合。模块灵活性 三种处理技术:分为简单、中级和复杂的处理方式。 模拟大部分水质问题:常规污染物,溶解氧、富营养化、温度;有毒污染物,有机物、简单的金属、汞等 局限性: WASP的研究对象为完全混合水体控制单元,比如排污口附近这种类型的问题不能模拟。 非水相:油的比重、粘度和水不一样。进入水体后,不同于水,WASP不能模拟。 干涸: 我们认为水体的容量是一定的,不变的。有很强的蒸发作用,对水体的容积有一个很显著的变化产生,这种情况WASP也是不适用的。很多水质模型都存在这种限制。 金属,重金属:很多过程是不能体现的。 WASP(Thewaterqualityanalysissimulationprogram,水质分析模拟程序)是EPA推荐使用的水质模型软件,使用较为广泛,能够模拟河流、湖泊、水库、河口等多种水体的稳态和非稳态的水质过程。
WASP powershell 脚本
powershell下非常好用,里面有说明文档,安装好就可以了,写powershell 脚本的必备良药
WASP 8.3风能资源分析软件
WAsP软件是用于风机微观选址的风能资源分析软件。可利用<em>wasp</em>软件对气象数据进行了分析,得到风数据统计表、风向玫瑰图和风频分布图。该数据结果为以后风电场选址中风能资源评估工作提供了重要依据,具有一定的参考意义。
WASP风资源评估软件
丹麦开发的风资源评估软件,行业应用最广的风资源评估软件。
WAsPMapEditor
<em>wasp</em>程序组件,地图工具,需要与主程序配合使用。
Wasp地图编辑器操作指南
指导新手掌握风资源计算软件中地图编辑器,快速掌握操作方法和步骤,非常实用的指导,时间长了也要拿出来看看,不然都会忘记的。
WAsP9.0注册版(主程序)
<em>wasp</em>是经典的风资源软件,也是仅有的可以通过特殊通道免费使用的风资源软件。由于网站对文件大小限制,软件几个部分分别上传,请按需下载。
WASP软件介绍
The Water Quality Analysis Simulation Pro-gram—(WASP6), an enhancement of the original WASP (Di Toro et al., 1983; Connolly and Winfield, 1984; Ambrose, R.B. et al., 1988). This model helps users interpret and predict water quality responses to natural phenomena and man-made pollution for various pollution management decisions. WASP6 is a dynamic compartment-modeling program for aquatic systems, including both the water column and the underlying benthos. WASP allows the user to investigate 1, 2, and 3 dimensional systems, and a variety of pollutant types. The state variables for the given modules are given in the table below. The time-varying processes of advection, dispersion, point and diffuse mass loading and boundary exchange are represented in the model. WASP also can be linked with hydrodynamic and sediment transport models that can provide flows, depths velocities, temperature, salinity and sediment fluxes.
WAsP软件介绍
目前丹麦RISO/DTU开发的WAsP软件主要包括WAsP软件和WAsP Engineering软件。WAsP软件是风能资源评估与风电场设计软件,该软件已经有20多年的历史,在世界各地得到广泛使用,对于平坦地形的风能资源分析结功能得到普遍认可,是世界上应用最广泛的风能资源分析软件。目前最新版本为WAsP 9.0
水质模拟软件 WASP7_5
美国流行的水质模拟软件,最新版本 WASP7_5 保证能用。免去去外国网站下载的麻烦。
风资源分析软件wasp入门实例
WASP软件分析风资源数据的基本操作,很实用
风资源分析软件WAsP入门实例
风资源分析软件WAsP入门实例,通过学习可以基本掌握风资源分析方法
WASP水质模型及其研究进展
WASP(The water quality analysis simulation program ,水质分析模拟程序)是 EPA推荐使用的水质模型软件 ,使用较为广泛 ,能够模拟河流、 湖泊、 水库、 河口等多种水体的稳态和非稳态的水质过程。介绍了 WASP 的组成模块 (DY NHY D、 EUTRO、 TOXI) 、 基本原理及 EUTRO 中8个指标之间的相互转化 ,最后介绍了该模型在国内外的应用和发展前景、方向。
IronWasp Web应用程序漏洞扫描
哎呀,web漏洞太多导致服务器老是被攻击,看来有效的测试对程序还是至关重要的!!!web程序是写的一个小项目:在java上用php的语法,比如$empty,$_get,$_post,$_file,这是非常有意思的,作者也曾经在java上用js,这实在是太帅了!!!(自己感觉不喜勿喷)好了废话不多说了,直接看看这个工具怎么用吧1.下载http://iron<em>wasp</em>.org/2.使用 下载以后解压文件
水质模拟wasp
水质模拟 <em>wasp</em> epa
WAsP Engineering操作
WAsP Engineering是WAsP的一个补充,通过本说明可以很好的了解WAsP Engineering软件的操作。
【PowerShell(1)】—— 介绍和安装及简单使用
PowerShell介绍owershell 是运行在windows机器上实现系统和应用程序管理自动化的命令行脚本环境。你可以把它看成是命令行提示符cmd.exe的扩充,不对,应当是颠覆。 powershell需要.NET环境的支持,同时支持.NET对象。微软之所以将Powershell 定位为Power,并不是夸大其词,因为它完全支持对象。其可读性,易用性,可以位居当前所有shell之首。 当前p...
百度整站源码 php wAsp源码 net源码 PHP源码 其它源码
百度整站源码 php wAsp源码 net源码 PHP源码 其它源码
渗透中的Powershell简单使用
powershell的简单使用。 一些脚本的开发 ,主要用于渗透
wasp使用说明文档
WAsP Engineering主要用于对复杂地形下的极端风速、风切变效应、流动的偏角、极端湍流强度进行评估,侧重于对风的特性以及由此带来的负载的研究,是对WASP软件的一个补充。 WAsP Engineering的核心流体模型已经在RISO实验室运行了20多年,并成为WASP软件的一个核心运算模型,而WAsP Engineering又在结合WAsP模型的基础上,发展了新的运算模型:粗糙度描述模型、粗糙度的变化模型、复杂地型产生的紊流等各种情形,预测复杂地形下50年极端风速的程序等。
WaSP.Engineering.v2.0
WaSP.Engineering.<em>wasp</em>组件
WASP稳态示例
WASP水质模型稳态示例数据等,WASP是EPA推荐水质模型。
wasp软件介绍应用 操作说明
<em>wasp</em>软件 WAsP Engineering主要用于对复杂地形下的极端风速、风切变效应、流动的偏角、极端湍流强度进行评估,侧重于对风的特性以及由此带来的负载的研究,是对WASP软件的一个补充。
WaterGEMS第二讲:水质模拟计算
上回书已经说道,水质模拟呢,就是怎么把水从水源干净地送到用水的人手里,那么应该怎么做呢,接下来我为大家一一道来。 首先打开WaterGEMS,新建一个任务,本人取名为justice.wtg。并绘制如下图所示的图形。按照第一讲的方法,打开Flextable,并填入高程,管长等信息。 2.在R-1与J-1节点之间绘制一个水泵,高程为7.6 3.双击水泵,并且在右边的菜单中,选择如
WAsP Sample Test Files
<em>wasp</em>文件,需要与<em>wasp</em>主程序及其他程序配合使用,对于程序的安装检验非常重要。
WASP水质模型有毒物质用例
WASP水质模型,EPA推荐模型;包含WASP有毒物质用例输入文件。
Wasp AppGen Pro
Wasp AppGen Pro手册
一文读懂 with ... as 原理
对于文件、数据库连接、socket 等系统资源而言,应用程序打开这些资源并执行完业务逻辑之后,必须做的一件事就是要关闭(断开)该资源。否则会一直占用资源,影响性能。 以向文件写入数据为例 普通版: f = open('file.txt', 'w') f.write('<em>wasp</em>vae') f.close() 这种写法会有一个潜在的问题,如果在调用 write 的过程中出现了...
WASP Model 流域例子
包含用于将 LSPC 链接到WASP水质模型的用例文件。包括 LSPC 模型, 输入文件, 水资源数据库, LSPC_2_WASP 电子表格程序用来创建链接文件, 和WASP输入文件。
WASP 源代码
WASP(Water Quality Analysis Simulation Program)(水质分析模拟)软件的源代码,是用fortran编的。
WASP的十大Web应用程序
WASP的十大Web风险 及对应的检测工具.
wasp8.1 破解器
WAsP破解器,适用于WAsP9及以下版本
WASP ADI工具
ADI 的<em>wasp</em> 工具,可以再PC端读取AD
Wasp nano cp(cpx)_manual
思凯利nano cp说明书,Wasp nano cp(cpx)_manual
关于 Systinet WASP
我在赛迪网 rnhttp://developer.ccidnet.com/pub/disp/Article?columnID=303&articleID=24748&pageNO=1rn看到有关 Systinet WASP 的介绍,有关于 WASP Advanced 与 <em>wasp</em>_demo 的,可是我到哪去下载这两个资源呢??rnrn我在 www.Systinet.com 上没有找到,请各位大侠帮忙
风电场优化软件OpenWind
美国AWSTrueWind公司开源风电场机位优化软件源代码 需要Ultimate++(upp)IDE和MS SDK或MingGW
power shell 操作键盘鼠标
# 操作键盘 $wshell = New-Object -ComObject wscript.shell # 操作鼠标 function Click-MouseButton { param([string]$Button, [switch]$help) $HelpInfo = @' Function : Click-MouseButton By : John Bartels
风资源评估软件windographer
风资源评估软件windographer,快速处理测风数据,一键生成风资源评估报告,风电行业技术评估必备软件
火电厂流程仿真软件
火电厂仿真软件 有兴趣的可以体验一下 flash格式可以后期自行修改含有公式和语音播报 谢谢各位师傅多多指教
ADuc812的开发工具
数据采集芯片ADuc812的开发工具,包含了代码下载工具wsd,测试工具<em>wasp</em>,还有仿真工具Adsim。 支持ADuc8XX系列的单片机用
攻击性Web测试框架(OWTF)是OWASP + PTES的重点,旨在联合优秀的工具使渗透测试更加高效,主要由Python编写
OWASP OWTF是一个专注于渗透测试效率和安全测试与OWASP测试指南(v3和v4),OWASP Top 10,PTES和NIST等安全标准一致的项目,因此测试者将有更多时间来    看大图,想出来    更有效地查找,验证和组合漏洞    有时间调查复杂的漏洞,如业务逻辑/架构漏洞或虚拟主机会话    对看起来有风险的地区进行更多的战术/目标模糊测试    尽管我们通常会给予测试的时间很短,...
著名的水质模拟软件,EFDC.本人亲测安装成功。
网上下了好多EFDC的程序,不是试用版就是不能用。这个是可以用的,但是仅限于一维。二维已经商业化了。
有没有哪位大侠用过WASP UDDI啊?
有没有人用过Systinet公司的WASP UDDI来构建注册中心啊?我下了一个<em>wasp</em>_uddi_4.6_sp1,但是安装的时候总是有错,在指定MS SQL SERVER 2000的JDBC驱动文件时,到底要指定什么文件啊?然后安装时就出现大概是rnInstall sqlcommand xml return java 1的错误,有哪位高人指点一下啊?
WAsP模型对宁夏风能评估的应用研究
:通过WAsP模型在宁夏区域风能评估的应用, 对所选宁夏区域4个站点进行逐日时间序列(1991 -01 -01 ~ 2007 -04 -30日)4次定时(02、08、14、20时)各时次的16方位平均风向频率图及逐日风速风向时 间序列,
WASP.climate.analyst
<em>wasp</em>是经典的风资源软件,也是仅有的可以通过特殊通道免费使用的风资源软件。由于网站对文件大小限制,软件几个部分分别上传,请按需下载。
模拟鼠标点击
原理 当用户在对话框上进行一系列动作时,背后的行为就是一个动作产生一个消息,从而引发一系列消息响应,我们可以利用PostMessage或SendMessage函数去发送相对应的消息,就可以完成模拟操作了。 PostMessage PostMessage消息原型: BOOL P...
请问谁有Free的UDDI 工具?象 WASP UDDI一样. 谢谢!
RT
FL Studio教程之Wasp XT合成器功能介绍
本文将采用图文结合的方式给FL Studio中的Wasp XT合成器的相关功能,感兴趣的朋友可以一起来交流哦。 从喷涌的岩浆到神秘的宇宙航行,Wasp都能提供相应的背景声,Wasp XT是一个3振荡器合成器,它包含一个FILTER(滤波器)栏,3个OSC(振荡器)栏,两个LFO(低频振荡器)栏,一个AMP ENV(放大器包络)栏,一个FILTERENV(滤波器包络)栏,一个MODENV(
谁有一些Free的 UDDI 工具,象WASP UDDI 一样? THX
RT
请问wasp uddi在哪里可以下啊?急。。。。。
谢谢了!!!!
OWASP测试指南(Owasp Testing Guide v4)中文高清-第4版-良心积分价
OWASP测试指南(O<em>wasp</em> Testing Guide v4)中文高清-第4版-良心积分价 OWASP安全测试指南-中文高清-第四版
水环境水质模型预测软件,一维模型、二维模型
水环境预测软件,水质预测模型,包含河流、湖库的一维水质模型、二维水质模型。内置各种模型公式和解释。针对环境影响评价、环评预测、饮用水水源保护区划分预测具有很好的帮助。
WAMPServer多站点配置
要配置多站点需要修改以下几点 1.修改文件httpd-vhosts.conf,文件路径:E:\wamp\bin\apache\apache2.4.23\conf\extra\httpd-vhosts.conf,增加两个站点 2.由于httpd-vhosts.conf是扩展文件,要确保httpd-vhosts.conf的配置的被引用的,有些版本默认是注释的。打开httpd.conf文件,路径
遗传单纯形混合算法在复杂环境模型参数识别中的应用
参数识别是数学模型应用的一个重要环节。为提高复杂环境模型参数识别的性能和效率,引入了遗传单纯形法(GASM),该方法融合了遗传算法和单纯形法两类算法的不同搜索机制,具有很强的广度搜索和深度搜索能力。本研究以密云水库水质模拟为例,将GASM算法应用于模拟地表水水质的WASP模型中10个参数的优化识别。计算结果表明,无论是没有扰动的情况还是有扰动的情况,GASM算法均高效可靠地搜索到水质模型参数的全局最优解,说明此方法应用于复杂环境模型参数搜索是可行的实用的。同时,通过不同算法的比较也说明了GASM算法在搜索性能和效率方面的优越性。
DVWA安装记录
本文主要对DVWA安装过程进行记录,对期间出现的问题进行了总结归纳,希望可以帮到你
【大杂烩】杂7杂8的东西
记录平时工作的内容和体会
利用Owasp Zap Proxy实现正对Web站点的扫描和漏洞发现功能
O<em>wasp</em>的功能与之前提过的Burp Pro Proxy相类,都具有流量代理、fuzz请求、抓取网页和自动扫描等功能,不同的是O<em>wasp</em>提供了更加简洁的操作界面,Burp Pro Proxy支持Burp扩展能够具有更强大的定制处理能力。 同metasploitable一样,O<em>wasp</em> Zap Proxy提供了一个存在漏洞的渗透测试演练工具OWASPBWA,我们可以通过这个虚拟机练习使用O<em>wasp</em> ...
Java Web服务开发
本人资源全部免费,更多资源可以查看我的上传资源 ======================================= 书 名:Java Web服务开发 作 者:[美]尼戈潘 等著,庞太刚,陶程 译 出 版 社:清华大学出版社 出版时间:2004-5-1 页 数:543 定 价:68.00 ISBN:9787302084440 内容简介:本书全面深入地探讨了下一代分布式计算技术—— Web服务,深入透彻地阐述了如何使用Java实现和部署Web服务,同时也全面介绍了与之相关的基础知识。在详细介绍了Web服务之后,本书还引导您探讨Web服务体系结构及其核心构件块,包括一些相关标准和技术。通过对本书的学习,您将学会如何使用Sun JWSDP 1.0 API开发Web服务,以及如何将J2EE应用程序发布为Web服务。 本书着重从概念、技术和实用技巧的角度展开论述: ·讨论Web服务标准的演变历程,包括ebXML的重要进展; ·展示使用Java的Web服务体系结构,以及如何根据现有的J2EE应用程序构建Web服务; ·学习并实现使用Sun JWSDP 1.0 API的案例分析; ·展示Java Web服务与Micorsoft.NET的互操作性; ·概述新出现的Web服务安全性标准,并展示如何在Web服务中实现安全性; ·提供使用Sun JWSDP 1.0、BEA Weblogic 7.0、Apache Axis 1.0B3、Systinet WASP 4.0、Exolab CASTOR、IBM XML安全套件和Micorsoft.NET的大量示例。 作者简介:Ramesh Nagappan是Sun Java中心的Enterprise Java架构师,拥有13年的从业经验,擅长于设计和实现基于Java、XML和COBRA的分布式计算体系结构,可适用于Internet应用程序、企业消息交换和Web服务。Ramesh还与其他作者合著了关于J2EE和EAI的一些书籍。
EFDC(The Environmental Fluid Dynamics Code)模型源码
EFDC(The Environmental Fluid Dynamics Code)模型是由威廉玛丽大学维吉尼亚海洋科学研究所(VIMS,Virginia Institute of Marine Science at the College of William and Mary)的John Hamrick等人开发的三维地表水水质数学模型,可实现河流、湖泊、水库、湿地系统、河口和海洋等水体的水动力学和水质模拟,是一个多参数有限差分模型。经过近20年的发展和完善,目前该模型已在大学,政府机关和环境咨询公司等组织中被广泛使用,并成功用于美国和欧洲其他国家100多个水体区域的研究,在我国已被应用于云南滇池水质模拟,重庆两江汇流水动力模拟、密云水库营养物模拟等以及内蒙古乌梁素海地区水体富营养化模拟等。[1] 该模型系统包括水动力、泥沙、有毒物质、水质、底质、风浪等模块,模拟计算过程中首先完成流场计算,获得三维流速场的时空分布特征,在此基础上计算泥沙迁移、冲淤作用,进而模拟受粘性泥沙吸附影响的各水质变量动态变化过程。为更好的拟合研究区地形条件,模型在水平方向除可采用传统的 直角坐标外还可在水平向使用正交曲线坐标,垂直方向采用σ坐标。 EFDC水动力学模块可计算如下内容:流速,示踪剂,温度,盐度,近岸羽流和漂流。水动力学模型输出变量可直接与水质,底泥迁移和毒性物质等模块耦合,作为物质运移的驱动条件。同时EFDC也提供了与WASP等软件的接口,输出可供水质模拟使用的.HYD文件。EFDC泥沙模块可进行多组分泥沙的模拟,根据在水体里面的迁移特征把泥沙分为悬移质和推移质;悬移质根据粒径大小分为粘性泥沙和非粘性泥沙,进而还可细分为若干组。可根据物理或经验模型模拟泥沙的沉降、沉积、冲刷及再悬浮等过程。EFDC有毒污染物模块可以模拟各类型污染物在水体中的迁移转化过程,该模块需要研究者针对特定有毒污染物提供具体反应过程设定反应系数。EFDC的水质模块,主要模拟水体中以藻类生长为中心的各变量间相互关系。而底质模块模拟沉积物与水体之间的物质交换过程。
Rave Player网页视频播放器
支持格式 视频: FLV, SWF, MPEG-4, 3GP 音频: MP3, AAC M4A. 支持播放列表、播放全屏、播放暂停 官方说明: A highly customizable and skin-able media player, which displays and plays collections of audio and/or video. Perfect for musicians, bands and anyone that needs to control the look and feel of their player to fit with their site design. Rave also offers custom playlist creation and editing, or you can manage what appears in the player by directing Rave to a folder full of media files on your site. Wimpy Rave v2.0.31 Jun. 25, 2012 - Corrected strict php warning reporting v2.0.30 Jun. 1, 2012 - Corrected scrubber issue for MP3 files without file size data (or servers that don't issue file size headers). v2.0.29 Apr. 30, 2012 - Changed fullscreen to true fullscreen. v2.0.28 Apr. 24, 2012 - Corrected issue with embedded reg code. - Retooled fullscreen and popout (internalized configs). v2.0.25 Apr. 3, 2012 - Corrected popup fullscreen issue for IE 9 and Chrome. - Adjusted resume to compensate for over under. - Updated swfobject within rave.js to v 2.2 v2.0.23a Jun. 7, 2011 - Updated getid3 library to v1.7.10 v2.0.23 Mar. 15, 2011 - Corrected issue with video resizing incorrectly for certain skins. v2.0.22 (Nov. 17, 2010) NOT RELEASED - Corrected unloader() prior to launching track, when multiple file kinds are mixed, potentially launch two streams. v2.0.21 (Nov. 9, 2010) - Corrected issue with scrolling display text. v2.0.19a (May 14, 2010) - Updated JS file for DOM element v2.0.19 (Feb. 1, 2010) - Fixed issue with "linkToWindow" for links in playlist. v2.0.18 (Jan. 27, 2010) - Corrected issue with fame EQ not working with M4A files v2.0.17 (Dec. 31, 2009) - Corrected issue with ampersands (& were converted to & ) in ecommerce URLs - Added feature: plugsBlockControls (undocumented) v2.0.16 (Nov. 24, 2009) - Corrected issue with extra playbar appearing randomly. - Corrected issue with playlist text not rendering the color correctly on in Firefox. v2.0.15 (Nov. 20, 2009) - Added m4v extension to video list. v2.0.14 (Nov. 12, 2009) - If wimpyApp contains a "?" then replace startupdirlist "?" with a "&". v2.0.13 (Nov. 12, 2009) - Added linkToWindow feature and handleLinkClick in rave.js. v2.0.11 (Nov. 7, 2009) - Added rave_reg.txt capabilities. v2.0.10 (Oct. 1, 2009) - Corrected issue with startPlayingOnload and SWF movies. Wimpy MP3 v6.0.33e (Sep. 26, 2012) - Updated OBJECT/EMBED code in basic-customizer.html. v6.0.33d (Sep. 20, 2012) - Fixed asc, dec in wimpy.php. v6.0.33 (Jul. 13, 2012) - Updated wimpy.php for PHP5 compatability. v6.0.33 (Jul. 26, 2011) - Updated javascript kit. v6.0.29 (Jul. 22, 2011) - Added javascript hook to remove track from playlist - Fixed issue with ecommerce javascript linking. v 6.0.28a (Jun. 7, 2011) - Updated getid3 library to v1.7.10 v 6.0.28 (Nov. 10, 2010) - Corrected issue with cover art + startOnLoad + XML playlists. v 6.0.26a (May 14, 2010) - Updated JS file for DOM element v 6.0.26 (Feb. 12, 2010) - Introduced the "Basic Customizer" - Updated copyright info in SWF. v 6.0.25 (JS files) (Dec. 11, 2009) - Updated Wimpy Button Bridge files to toggle writing returned data to the page (example #6) using a variable. v 6.0.25 (Dec. 2, 2009) - Corrected ecommerce button issue. (buyme() argument invalid) v 6.0.24 (Nov. 24, 2009) - Corrected playlist text coloring issue when wmode=transparent v 6.0.23 (Nov. 10, 2009) - Added external link -> javascript capabilities. - Updated JavaScript kit. - Added handleLinkClick functionality v 6.0.22 (Nov. 7, 2009) - Added wimpy_reg.txt capabilities - Updated wimpy.js to include wmode-tranparent - Updated Javascript Kit: v 6.0.18 (Oct. 31, 2009) - Updated wimpy.js and wimpy.swf - Normalized DOM with wimpy_getWimpyByID - Corrected wimpy_addTrack after using wimpy_clearPlaylist - Added wimpy_trackStoppedManually v 6.0.17 (Oct. 13, 2009) - Added "wimpyStartFolder" configuration option. Wimpy Button v 4.1.8 (Aug. 20, 2012) - Correct issue with replaying M4A file. v 4.1.7 (Dec. 10, 2011) - Various updates and fixes. v 4.1.5 (Dec. 29, 2009) - Corrected issue with null request while clearing loaded tracks. v 4.1.4 (Dec. 1, 2009) - Included wimpyButtonTrackDone to JS functions. Edited wimpy button bridge to include a function to reset buttons when done. v 4.1.3 (Nov. 7, 2009) - Added wimpy_button_reg.txt capabilities v 4.1.2 (Sept. 22, 2009) - Fixed "stop other sounds" ( SWF fires wimpyButtonTrackStarted, but that function was removed during bridge build) -- re-added the function. - Updated wimpy button bridge. - Added option for JS pause/play when stopping other tracks. v 4.1.1 (Aug. 16, 2009) - Created Javascript hooks - Incorporated AAC, M4A file types - Created Wimpy Button Bridge for Javascript only activity. Wimpy Wasp v4.0.123 (Oct. 14, 2010) - Varius updates and minor fixes. v 4.0.118 (Feb. 4, 2010) - Corrected issue with "Limit playback time (tl)" for MP3 files. v 4.0.117 (Nov. 20, 2009) - Added m4v extension to video list. v 4.0.116 (Nov. 7, 2009) - Updated <em>wasp</em>.js - Standardized DOM - Added <em>wasp</em>StopOthers - Added <em>wasp</em>_reg.txt capabilities - Fixed buffering indicator issue. Wasp Publisher v4.0.119a (Jun. 9, 2012) - Updated SWF object within <em>wasp</em>.js v4.0.119 (Dec. 3, 2010) - Updated EXE wrapper - Removed references to mProj (output pushed errors on loading mProj). - Rebuild MDM XML. v 4.0.118fc (May 14, 2010) - Updated JS file for DOM element v 4.0.118b (Feb. 4, 2010) - Updated <em>wasp</em>.swf to v 4.0.118 v 4.0.118a (Dec. 11, 2009) - Updated app wrapper to correct issue with contextual menu (copy and paste menu). v 4.0.118 (Nov. 20, 2009) - Added m4v extension to video list. v 4.0.117 (Nov. 7, 2009) - Added <em>wasp</em>_reg.txt output - Updated <em>wasp</em>.swf and <em>wasp</em>.js to v 4.0.116 - Fixed HTML output to scroll javascript links, removed <em>wasp</em>_prev (use_setPlayPercent instead) - Using EXE (Z) output v 3.0.14 + Flash player 10 in app. v 4.0.116b (Aug. 8, 2009) - Added "wmode" param to <em>wasp</em>.js for z-index layering. Wimpy Button Maker v 1.42n (Aug. 20, 2012) - Updated the Wimpy Button SWF file to version 4.1.8 v 1.42m (Sep. 16, 2011) - Updated the Wimpy Button SWF file to version 4.1.7 v 1.42k (Oct. 6, 2010) - Updated the Wimpy Button SWF file to version 4.1.6 v 1.42j (Jun. 21, 2010) - Corrected wimpy_button.js bug. v 1.42i (Dec. 29, 2009) - Updated the Wimpy Button SWF file to version 4.1.5 v 1.42h (Dec. 1, 2009) - Updated the Wimpy Button SWF file to version 4.1.4 v 1.42e (Nov. 7, 2009) - Updated the Wimpy Button SWF file to version 4.1.3 v 1.42d (Sept. 22, 2009) - Updated the Wimpy Button SWF file to version 4.1.1 v 1.42c (Aug. 16, 2009) - Updated the Wimpy Button SWF file to version 4.1.1 Wimpy FLV Player v 4.0.119 (3.0.13) (Sep. 8, 2011) - Fixed issue with selecting files using the "open" dialog on the Mac. v 4.0.116 (3.0.11) (Feb. 16, 2010) - Added the media's file name to the app title bar. - Fixed issue with player disappearing when audio plays. v 4.0.106 Playlister v5.0.8 (Sept. 21, 2010) - New wrapper: loadClip(<em>wasp</em>) doesn't need path. - Dropped ID3 reading and auto image search and load (causing App to freeze). v5.0.6a (Sept. 19, 2010) - Updated EXE/APP wrapper (using v4) v 5.0.6 (Nov. 17, 2009) - Fixed Mac browse for cover art issue. Skin Machine for Rave v3.0.166 (May 4, 2011) - Rebuilt exe/app with latest toolset for increased stability. (4-64/30) v3.0.164 - Rebuilt exe/app with latest toolset for increased stability. v3.0.160 - Rebuilt exe/app with latest toolset for increased stability. Skin Machine for MP3 v2.1.32a (Dec. 3, 2010) - Rebuilt app on a mac with latest toolset for increased stability. v 2.1.32 - Minor bug fixes. Wimpy SQL v2.1.2 (Jul. 12, 2012) - Updated getid3 library to version 1.9.3-20111213 - Upgraded for PHP v5 compatibility (added msql-i layer) - Updated Wimpy MP3 Player to version 6.0.33 - Updated OBJECT/EMBED codes - Various other fixes v1.0.53a (Aug. 25, 2010) - Updated "force download" headers. v1.0.53 (Jul. 28, 2010) - Fixed bug issue with adding items to playlist if playlist was scrolled down. - Updated wimpy.swf to v6.0.26 v1.0.52a (Nov. 7, 2009) - Updated wimpy.swf with version 6.0.22 v1.0.52 (Jan. 15, 2009) - Corrected issue with failure to start up with "Flash Player 10" plugin. - Updated included Wimpy MP3 Player files to v6.0.16 Wimpy SQL ED v3.0 (Jul. 12, 2012) - Updated for PHP 5 compatability. Wimpy Button Bridge v1.0.8 (Aug. 20, 2012) - Updated wimpy_button.swf to v 4.1.8 v1.0.7 (Jul. 26, 2011) - Reworked onload and IE issues when in quirks mode. v 1.0.6 (Jul. 19, 2011) - Incorporated configuration options into the wimpyButtonBridge.js file. v 1.0.5 (Jul. 15, 2011) - Removed SWFobject rendering (due to incompatibilities across browsers with SWFobject 1.5). Using "static" rendering (newer object classid method). - Added check for local examples. - Added onload handler Wimpy MP3 Javascript Controls v3.0.8 (Jul, 26, 2011) - Updated removeTrack() behavior of current track playing is removed. v3.0.6 (Jul 23, 2011) - Fixed issue with wimpy_removeTrack(). - Updated wimpy.swf to version 6.0.30 v3.0.5 (Jul 22, 2011) - Added javascript hook to remove track from playlist - Fixed issue with ecommerce javascript linking. - Updated wimpy.swf to version 6.0.29 Rave WordPress Plugin v1.0.8 Sep. 8, 2011 - Fixed IE bug where form lists (combo boxes) we're not getting populated properly. - Fixed issue with manual list of URLs as playlist. v1.0.7 Jul. 20, 2011 - Fixed IE bug "value is null" error message. v1.0.6 Jul. 19, 2011 - Updated dragSort.js file to v0.4.3 - Corrected issue with not being able to delete items from a playlist. v1.0.5 Jul. 15, 2011 - Fixed playlist sorting issue, where the playlists were not returned in the proper order. v1.0.4 Jul. 12, 2011 - Fixed issue with setting "Sort Order" - Included version detection on "about" page. v1.0.2 May 15, 2011 - Original release
Handbook of Research on Soft Computing and Nature-Inspired Algorithms
Soft computing and nature-inspired computing both play a significant role in developing a better understanding to machine learning. When studied together, they can offer new perspectives on the learning process of machines. The Handbook of Research on Soft Computing and Nature-Inspired Algorithms is an essential source for the latest scholarly research on applications of nature-inspired computing and soft computational systems. Featuring comprehensive coverage on a range of topics and perspectives such as swarm intelligence, speech recognition, and electromagnetic problem solving, this publication is ideally designed for students, researchers, scholars, professionals, and practitioners seeking current research on the advanced workings of intelligence in computing systems. Chapter 1 ApplicationofNatured-InspiredAlgorithmsfortheSolutionofComplexElectromagnetic Problems................................................................................................................................................. 1 Massimo Donelli, University of Trento, Italy Inthelastdecadenature-inspiredOptimizerssuchasgeneticalgorithms(GAs),particleswarm(PSO), antcolony(ACO),honeybees(HB),bacteriafeeding(BFO),firefly(FF),batalgorithm(BTO),invasive weed(IWO)andothersalgorithms,hasbeensuccessfullyadoptedasapowerfuloptimizationtools inseveralareasofappliedengineering,andinparticularforthesolutionofcomplexelectromagnetic problems.Thischapterisaimedatpresentinganoverviewofnatureinspiredoptimizationalgorithms (NIOs)asappliedtothesolutionofcomplexelectromagneticproblemsstartingfromthewell-known geneticalgorithms(GAs)uptorecentcollaborativealgorithmsbasedonsmartswarmsandinspired byswarmofinsects,birdsorflockoffishes.Thefocusofthischapterisontheuseofdifferentkind ofnaturedinspiredoptimizationalgorithmsforthesolutionofcomplexproblems,inparticulartypical microwavedesignproblems,inparticularthedesignandmicrostripantennastructures,thecalibration ofmicrowavesystemsandotherinterestingpracticalapplications.Startingfromadetailedclassification andanalysisofthemostusednaturedinspiredoptimizers(NIOs)thischapterdescribesthenotonly thestructuresofeachNIObutalsothestochasticoperatorsandthephilosophyresponsibleforthe correctevolutionoftheoptimizationprocess.Theoreticaldiscussionsconcernedconvergenceissues, parameterssensitivityanalysisandcomputationalburdenestimationarereportedaswell.Successively abriefreviewonhowdifferentresearchgroupshaveappliedorcustomizeddifferentNIOsapproaches forthesolutionofcomplexpracticalelectromagneticproblemrangingfromindustrialuptobiomedical applications.ItisworthnoticedthatthedevelopmentofCADtoolsbasedonNIOscouldprovidethe engineersanddesignerswithpowerfultoolsthatcanbethesolutiontoreducethetimetomarketof specific devices, (such as modern mobile phones, tablets and other portable devices) and keep the commercialpredominance:sincetheydonotrequireexpertengineersandtheycanstronglyreducethe computationaltimetypicalofthestandardtrialerrorsmethodologies.Suchusefulautomaticdesigntools basedonNIOshavebeentheobjectofresearchsincesomedecadesandtheimportanceofthissubject iswidelyrecognized.Inordertoapplyanaturedinspiredalgorithm,theproblemisusuallyrecastas aglobaloptimizationproblem.Formulatedinsuchaway,theproblemcanbeefficientlyhandledby naturedinspiredoptimizerbydefiningasuitablecostfunction(singleormulti-objective)thatrepresent thedistancebetweentherequirementsandtheobtainedtrialsolution.Thedeviceunderdevelopment  canbeanalyzedwithclassicalnumericalmethodologiessuchasFEM,FDTD,andMoM.Asacommon feature,theseenvironmentsusuallyintegrateanoptimizerandacommercialnumericalsimulator.The chapterendswithopenproblemsanddiscussiononfutureapplications. Chapter 2 AComprehensiveLiteratureReviewonNature-InspiredSoftComputingandAlgorithms:Tabular andGraphicalAnalyses........................................................................................................................ 34 Bilal Ervural, Istanbul Technical University, Turkey Beyzanur Cayir Ervural, Istanbul Technical University, Turkey Cengiz Kahraman, Istanbul Technical University, Turkey SoftComputingtechniquesarecapableofidentifyinguncertaintyindata,determiningimprecisionof knowledge,andanalyzingill-definedcomplexproblems.Thenatureofrealworldproblemsisgenerally complexandtheircommoncharacteristicisuncertaintyowingtothemultidimensionalstructure.Analytical modelsareinsufficientinmanagingallcomplexitytosatisfythedecisionmakers’expectations.Under thisviewpoint,softcomputingprovidessignificantflexibilityandsolutionadvantages.Inthischapter, firstly,themajorsoftcomputingmethodsareclassifiedandsummarized.Thenacomprehensivereviewof eightnatureinspired–softcomputingalgorithmswhicharegeneticalgorithm,particleswarmalgorithm, antcolonyalgorithms,artificialbeecolony,fireflyoptimization,batalgorithm,cuckooalgorithm,and greywolfoptimizeralgorithmarepresentedandanalyzedundersomedeterminedsubjectheadings (classificationtopics)inadetailedway.Thesurveyfindingsaresupportedwithcharts,bargraphsand tablestobemoreunderstandable. Chapter 3 SwarmIntelligenceforElectromagneticProblemSolving................................................................... 69 Luciano Mescia, Politecnico di Bari, Italy Pietro Bia, EmTeSys Srl, Italy Diego Caratelli, The Antenna Company, The Netherlands & Tomsk Polytechnic University, Russia Johan Gielis, University of Antwerp, Belgium ThechapterwilldescribethepotentialoftheswarmintelligenceandinparticularquantumPSO-based algorithm,tosolvecomplicatedelectromagneticproblems.Thistaskisaccomplishedthroughaddressing the design and analysis challenges of some key real-world problems. A detailed definition of the conventionalPSOanditsquantum-inspiredversionarepresentedandcomparedintermsofaccuracyand computationalburden.Sometheoreticaldiscussionsconcerningtheconvergenceissuesandasensitivity analysisontheparametersinfluencingthestochasticprocessarereported. Chapter 4 ParameterSettingsinParticleSwarmOptimization........................................................................... 101 Snehal Mohan Kamalapur, K. K. Wagh Institute of Engineering Education and Research, India Varsha Patil, Matoshree College of Engineering and Research Center, India Theissueofparametersettingofanalgorithmisoneofthemostpromisingareasofresearch.Particle SwarmOptimization(PSO)ispopulationbasedmethod.TheperformanceofPSOissensitivetothe parametersettings.Intheliteratureofevolutionarycomputationtherearetwotypesofparametersettings  - parametertuningandparametercontrol.Staticparametertuningmayleadtopoorperformanceas optimalvaluesofparametersmaybedifferentatdifferentstagesofrun.Thisleadstoparametercontrol. Thischapterhastwo-foldobjectivestoprovideacomprehensivediscussiononparametersettingsandon parametersettingsofPSO.Theobjectivesaretostudyparametertuningandcontrol,togettheinsight ofPSOandimpactofparameterssettingsforparticlesofPSO. Chapter 5 ASurveyofComputationalIntelligenceAlgorithmsandTheirApplications...................................133 Hadj Ahmed Bouarara, Dr. Tahar Moulay University of Saida, Algeria Thischaptersubscribesintheframeworkofananalyticalstudyaboutthecomputationalintelligence algorithms.Thesealgorithmsarenumerousandcanbeclassifiedintwogreatfamilies:evolutionary algorithms(geneticalgorithms,geneticprogramming,evolutionarystrategy,differentialevolutionary, paddyfieldalgorithm)andswarmoptimizationalgorithms(particleswarmoptimisationPSO,antcolony optimization(ACO),bacteriaforagingoptimisation,wolfcolonyalgorithm,fireworksalgorithm,bat algorithm,cockroachescolonyalgorithm,socialspidersalgorithm,cuckoosearchalgorithm,<em>wasp</em>swarm optimisation,mosquitooptimisationalgorithm).Wehavedetailedeachalgorithmfollowingastructured organization(theoriginofthealgorithm,theinspirationsource,thesummary,andthegeneralprocess). Thispaperisthefruitofmanyyearsofresearchintheformofsynthesiswhichgroupsthecontributions proposedbyvariousresearchersinthisfield.Itcanbethestartingpointforthedesigningandmodelling newalgorithmsorimprovingexistingalgorithms. Chapter 6 OptimizationofProcessParametersUsingSoftComputingTechniques:ACaseWithWire ElectricalDischargeMachining..........................................................................................................177 Supriyo Roy, Birla Institute of Technology, India Kaushik Kumar, Birla Institute of Technology, India J. Paulo Davim, University of Aveiro, Portugal MachiningofhardmetalsandalloysusingConventionalmachininginvolvesincreaseddemandof time,energyandcost.Itcausestoolwearresultinginlossofqualityoftheproduct.Non-conventional machining,ontheotherhandproducesproductwithminimumtimeandatdesiredlevelofaccuracy.In thepresentstudy,EN19steelwasmachinedusingCNCWireElectricaldischargemachiningwithpredefinedprocessparameters.MaterialRemovalRateandSurfaceroughnesswereconsideredasresponses forthisstudy.Thepresentoptimizationproblemissingleandaswellasmulti-response.Consideringthe complexitiesofthispresentproblem,experimentaldataweregeneratedandtheresultswereanalyzed byusingTaguchi,GreyRelationalAnalysisandWeightedPrincipalComponentAnalysisundersoft computingapproach.Responsesvarianceswiththevariationofprocessparameterswerethoroughly studiedandanalyzed;also‘bestoptimalvalues’wereidentified.Theresultshowsanimprovementin responsesfrommeantooptimalvaluesofprocessparameters.  Chapter 7 AugmentedLagrangeHopfieldNetworkforCombinedEconomicandEmissionDispatchwith FuelConstraint.................................................................................................................................... 221 Vo Ngoc Dieu, Ho Chi Minh City University of Technology, Vietnam Tran The Tung, Ho Chi Minh City University of Technology, Vietnam This chapter proposes an augmented Lagrange Hopfield network (ALHN) for solving combined economicandemissiondispatch(CEED)problemwithfuelconstraint.IntheproposedALHNmethod, theaugmentedLagrangefunctionisdirectlyusedastheenergyfunctionofcontinuousHopfieldneural network(HNN),thusthismethodcanproperlyhandleconstraintsbybothaugmentedLagrangefunction andsigmoidfunctionofcontinuousneuronsintheHNN.Fordealingwiththebi-objectiveeconomic dispatchproblem,theslopeofsigmoidfunctioninHNNisadjustedtofindthePareto-optimalfrontand thenthebestcompromisesolutionfortheproblemwillbedeterminedbyfuzzy-basedmechanism.The proposedmethodhasbeentestedonmanycasesandtheobtainedresultsarecomparedtothosefrom othermethodsavailabletheliterature.Thetestresultshaveshownthattheproposedmethodcanfind goodsolutionscomparedtotheothersforthetestedcases.Therefore,theproposedALHNcouldbea favourableimplementationforsolvingtheCEEDproblemwithfuelconstraint. Chapter 8 SpeakerRecognitionWithNormalandTelephonicAssameseSpeechUsingI-Vectorand Learning-BasedClassifier................................................................................................................... 256 Mridusmita Sharma, Gauhati University, India Rituraj Kaushik, Tezpur University, India Kandarpa Kumar Sarma, Gauhati University, India Speaker recognition is the task of identifying a person by his/her unique identification features or behaviouralcharacteristicsthatareincludedinthespeechutteredbytheperson.Speakerrecognition dealswiththeidentityofthespeaker.Itisabiometricmodalitywhichusesthefeaturesofthespeaker thatisinfluencedbyone’sindividualbehaviouraswellasthecharacteristicsofthevocalcord.Theissue becomesmorecomplexwhenregionallanguagesareconsidered.Here,theauthorsreportthedesignof aspeakerrecognitionsystemusingnormalandtelephonicAssamesespeechfortheircasestudy.Intheir work,theauthorshaveimplementedi-vectorsasfeaturestogenerateanoptimalfeaturesetandhaveused theFeedForwardNeuralNetworkfortherecognitionpurposewhichgivesafairlyhighrecognitionrate. Chapter 9 ANewSVMMethodforRecognizingPolarityofSentimentsinTwitter.......................................... 281 Sanjiban Sekhar Roy, VIT University, India Marenglen Biba, University of New York – Tirana, Albania Rohan Kumar, VIT University, India Rahul Kumar, VIT University, India Pijush Samui, NIT Patna, India Onlinesocialnetworkingplatforms,suchasWeblogs,microblogs,andsocialnetworksareintensively beingutilizeddailytoexpressindividual’sthinking.Thispermitsscientiststocollecthugeamountsof dataandextractsignificantknowledgeregardingthesentimentsofalargenumberofpeopleatascale thatwasessentiallyimpracticalacoupleofyearsback.Therefore,thesedays,sentimentanalysishasthe potentialtolearnsentimentstowardspersons,objectandoccasions.Twitterhasincreasinglybecome  a significantsocialnetworkingplatformwherepeoplepostmessagesofupto140charactersknownas ‘Tweets’.Tweetshavebecomethepreferredmediumforthemarketingsectorasuserscaninstantlyindicate customersuccessorindicatepublicrelationsdisasterfarmorequicklythanawebpageortraditional mediadoes.Inthispaper,wehaveanalyzedtwitterdataandhavepredictedpositiveandnegativetweets withhighaccuracyrateusingsupportvectormachine(SVM). Chapter 10 AutomaticGenerationControlofMulti-AreaInterconnectedPowerSystemsUsingHybrid EvolutionaryAlgorithm...................................................................................................................... 292 Omveer Singh, Maharishi Markandeshwar University, India Anewtechniqueofevaluatingoptimalgainsettingsforfullstatefeedbackcontrollersforautomatic generationcontrol(AGC)problembasedonahybridevolutionaryalgorithms(EA)i.e.geneticalgorithm (GA)-simulatedannealing(SA)isproposedinthischapter.ThehybridEAalgorithmcantakedynamic curveperformanceashardconstraintswhicharepreciselyfollowedinthesolutions.Thisisincontrast tothemodernandsinglehybridevolutionarytechniquewheretheseconstraintsaretreatedassoft/hard constraints.Thistechniquehasbeeninvestigatedonanumberofcasestudiesandgivessatisfactorysolutions. Thistechniqueisalsocomparedwithlinearquadraticregulator(LQR)andGAbasedproportionalintegral (PI)controllers.Thisprovestobeagoodalternativeforoptimalcontroller’sdesign.Thistechniquecan beeasilyenhancedtoincludemorespecificationsviz.settlingtime,risetime,stabilityconstraints,etc. Chapter 11 MathematicalOptimizationbyUsingParticleSwarmOptimization,GeneticAlgorithm,and DifferentialEvolutionandItsSimilarities.......................................................................................... 325 Shailendra Aote, Ramdeobaba College of Engineering and Management, India Mukesh M. Raghuwanshi, Yeshwantrao Chavan College of Engineering, India Tosolvetheproblemsofoptimization,variousmethodsareprovidedindifferentdomain.Evolutionary computing(EC)isoneofthemethodstosolvetheseproblems.MostlyusedECtechniquesareavailable likeParticleSwarmOptimization(PSO),GeneticAlgorithm(GA)andDifferentialEvolution(DE). Thesetechniqueshavedifferentworkingstructurebuttheinnerworkingstructureissame.Different namesandformulaearegivenfordifferenttaskbutultimatelyalldothesame.Herewetriedtofindout thesimilaritiesamongthesetechniquesandgivetheworkingstructureineachstep.Allthestepsare providedwithproperexampleandcodewritteninMATLAB,forbetterunderstanding.Herewestarted ourdiscussionwithintroductionaboutoptimizationandsolutiontooptimizationproblemsbyPSO,GA andDE.Finally,wehavegivenbriefcomparisonofthese. Chapter 12 GA_SVM:AClassificationSystemforDiagnosisofDiabetes.......................................................... 359 Dilip Kumar Choubey, Birla Institute of Technology Mesra, India Sanchita Paul, Birla Institute of Technology Mesra, India Themodernsocietyispronetomanylife-threateningdiseaseswhichifdiagnosisearlycanbeeasily controlled.Theimplementationofadiseasediagnosticsystemhasgainedpopularityovertheyears.The mainaimofthisresearchistoprovideabetterdiagnosisofdiabetes.Therearealreadyseveralexisting methods,whichhavebeenimplementedforthediagnosisofdiabetes.Inthismanuscript,firstly,Polynomial Kernel,RBFKernel,SigmoidFunctionKernel,LinearKernelSVMusedfortheclassificationofPIDD.  SecondlyGAusedasanAttributeselectionmethodandthenusedPolynomialKernel,RBFKernel, SigmoidFunctionKernel,LinearKernelSVMonthatselectedattributesofPIDDforclassification.So, herecomparedtheresultswithandwithoutGAinPIDD,andLinearKernelprovedbetteramongallof thenotedaboveclassificationmethods.ItdirectlyseemsinthepaperthatGAisremovinginsignificant features,reducingthecostandcomputationtimeandimprovingtheaccuracy,ROCofclassification. Theproposedmethodcanbealsousedforotherkindsofmedicaldiseases. Chapter 13 TheInsectsofNature-InspiredComputationalIntelligence............................................................... 398 Sweta Srivastava, B.I.T. Mesra, India Sudip Kumar Sahana, B.I.T. Mesra, India Thedesirablemeritsoftheintelligentcomputationalalgorithmsandtheinitialsuccessinmanydomains haveencouragedresearcherstoworktowardstheadvancementofthesetechniques.Amajorplunge inalgorithmicdevelopmenttosolvetheincreasinglycomplexproblemsturnedoutasbreakthrough towardsthedevelopmentofcomputationalintelligence(CI)techniques.Natureprovedtobeoneofthe greatestsourcesofinspirationfortheseintelligentalgorithms.Inthischapter,computationalintelligence techniquesinspiredbyinsectsarediscussed.Thesetechniquesmakeuseoftheskillsofintelligent agentbymimickinginsectbehaviorsuitablefortherequiredproblem.Thediversitiesinthebehaviorof theinsectfamiliesandsimilaritiesamongthemthatareusedbyresearchersforgeneratingintelligent techniquesarealsodiscussedinthischapter. Chapter 14 Bio-InspiredComputationalIntelligenceandItsApplicationtoSoftwareTesting............................ 429 Abhishek Pandey, UPES Dehradun, India Soumya Banerjee, BIT Mesra, India Bioinspiredalgorithmsarecomputationalprocedureinspiredbytheevolutionaryprocessofnature andswarmintelligencetosolvecomplexengineeringproblems.Intherecenttimesithasgainedmuch popularityintermsofapplicationstodiverseengineeringdisciplines.Nowadaysbioinspiredalgorithms arealsoappliedtooptimizethesoftwaretestingprocess.Inthischapterauthorswilldiscusssomeof thepopularbioinspiredalgorithmsandalsogivestheframeworkofapplicationofthesealgorithmsfor softwaretestingproblemssuchastestcasegeneration,testcaseselection,testcaseprioritization,test caseminimization.Bioinspiredcomputationalalgorithmsincludesgeneticalgorithm(GA),genetic programming (GP), evolutionary strategies (ES), evolutionary programming (EP) and differential evolution(DE)intheevolutionaryalgorithmscategoryandAntcolonyoptimization(ACO),Particle swarmoptimization(PSO),ArtificialBeeColony(ABC),Fireflyalgorithm(FA),Cuckoosearch(CS), Batalgorithm(BA)etc.intheSwarmIntelligencecategory(SI).  Chapter 15 Quantum-InspiredComputationalIntelligenceforEconomicEmissionDispatchProblem.............. 445 Fahad Parvez Mahdi, Universiti Teknologi Petronas, Malaysia Pandian Vasant, Universiti Teknologi Petronas, Malaysia Vish Kallimani, Universiti Teknologi Petronas, Malaysia M. Abdullah-Al-Wadud, King Saud University, Saudi Arabia Junzo Watada, Universiti Teknologi Petronas, Malaysia Economicemissiondispatch(EED)problemsareoneofthemostcrucialproblemsinpowersystems. Growingenergydemand,limitedreservesoffossilfuelandglobalwarmingmakethistopicintothe centerofdiscussionandresearch.Inthischapter,wewilldiscusstheuseandscopeofdifferentquantum inspiredcomputationalintelligence(QCI)methodsforsolvingEEDproblems.Wewillevaluateeach previouslyusedQCImethodsforEEDproblemanddiscusstheirsuperiorityandcredibilityagainst othermethods.WewillalsodiscussthepotentialityofusingotherquantuminspiredCImethodslike quantumbatalgorithm(QBA),quantumcuckoosearch(QCS),andquantumteachingandlearningbased optimization(QTLBO)techniqueforfurtherdevelopmentinthisarea. Chapter 16 IntelligentExpertSystemtoOptimizetheQuartzCrystalMicrobalance(QCM)Characterization Test:IntelligentSystemtoOptimizetheQCMCharacterizationTest............................................... 469 Jose Luis Calvo-Rolle, University of A Coruña, Spain José Luis Casteleiro-Roca, University of A Coruña, Spain María del Carmen Meizoso-López, University of A Coruña, Spain Andrés José Piñón-Pazos, University of A Coruña, Spain Juan Albino Mendez-Perez, Universidad de La Laguna, Spain Thischapterdescribesanapproachtoreducesignificantlythetimeinthefrequencysweeptestofa QuartzCrystalMicrobalance(QCM)characterizationmethodbasedontheresonanceprincipleofpassive components.Onthistest,thespenttimewaslarge,becauseitwasnecessarycarryoutabigfrequency sweepduetothefactthattheresonancefrequencywasunknown.Moreover,thisfrequencysweephas greatstepsandconsequentlylowaccuracy.Then,itwasnecessarytoreducethesweepsanditssteps graduallywiththeaimtoincreasetheaccuracyandtherebybeingabletofindtheexactfrequency.An intelligentexpertsystemwascreatedasasolutiontothedisadvantagedescribedofthemethod.This modelprovidesamuchsmallerfrequencyrangethantheinitiallyemployedwiththeoriginalproposal. Thisfrequencyrangedependsofthecircuitcomponentsofthemethod.Then,thankstothenewapproach oftheQCMcharacterizationisachievedbetteraccuracyandthetesttimeisreducedsignificantly. Chapter 17 OptimizationThroughNature-InspiredSoft-ComputingandAlgorithmonECGProcess................ 489 Goutam Kumar Bose, Haldia Institute of Technology, India Pritam Pain, Haldia Institute of Technology, India Inthepresentresearchworkselectionofsignificantmachiningparametersdependingonnature-inspired algorithmisprepared,duringmachiningalumina-aluminuminterpenetratingphasecompositesthrough electrochemical grinding process. Here during experimentation control parameters like electrolyte concentration(C),voltage(V),depthofcut(D)andelectrolyteflowrate(F)areconsidered.Theresponse dataareinitiallytrainedandtestedapplyingArtificialNeuralNetwork.Theparadoxicalresponseslike  highermaterialremovalrate(MRR),lowersurfaceroughness(Ra),lowerovercut(OC)andlowercutting force(Fc)areaccomplishedindividuallybyemployingCuckooSearchAlgorithm.Amultiresponse optimizationforalltheresponseparametersiscompiledprimarilybyusingGeneticalgorithm.Finally, inordertoachieveasinglesetofparametriccombinationforalltheoutputssimultaneouslyfuzzy basedGreyRelationalAnalysistechniqueisadopted.Thesenature-drivensoftcomputingtechniques corroborateswellduringtheparametricoptimizationofECGprocess. Chapter 18 AnOverviewoftheLastAdvancesandApplicationsofArtificialBeeColonyAlgorithm.............. 520 Airam Expósito Márquez, University of La Laguna, Spain Christopher Expósito-Izquierdo, University of La Laguna, Spain SwarmIntelligenceisdefinedascollectivebehaviorofdecentralizedandself-organizedsystemsofa naturalorartificialnature.Inthelastyearsandtoday,SwarmIntelligencehasproventobeabranchof ArtificialIntelligencethatisabletosolvingefficientlycomplexoptimizationproblems.SomeofwellknownexamplesofSwarmIntelligenceinnaturalsystemsreportedintheliteraturearecolonyofsocial insectssuchasbeesandants,birdflocks,fishschools,etc.Inthisrespect,ArtificialBeeColonyAlgorithm isanatureinspiredmetaheuristic,whichimitatesthehoneybeeforagingbehaviourthatproducesan intelligentsocialbehaviour.ABChasbeenusedsuccessfullytosolveawidevarietyofdiscreteand continuousoptimizationproblems.InordertofurtherenhancethestructureofArtificialBeeColony, thereareavarietyofworksthathavemodifiedandhybridizedtoothertechniquesthestandardversion ofABC.Thisworkpresentsareviewpaperwithasurveyofthemodifications,variantsandapplications oftheArtificialBeeColonyAlgorithm. Chapter 19 ASurveyoftheCuckooSearchandItsApplicationsinReal-WorldOptimizationProblems........... 541 Christopher Expósito-Izquierdo, University of La Laguna, Spain Airam Expósito-Márquez, University of La Laguna, Spain ThechapterathandseekstoprovideageneralsurveyoftheCuckooSearchAlgorithmanditsmost highlightedvariants.TheCuckooSearchAlgorithmisarelativelyrecentnature-inspiredpopulationbasedmeta-heuristicalgorithmthatisbaseduponthelifestyle,egglaying,andbreedingstrategyof somespeciesofcuckoos.Inthiscase,theLévyflightisusedtomovethecuckooswithinthesearch spaceoftheoptimizationproblemtosolveandobtainasuitablebalancebetweendiversificationand intensification.Asdiscussedinthischapter,theCuckooSearchAlgorithmhasbeensuccessfullyapplied toawiderangeofheterogeneousoptimizationproblemsfoundinpracticalapplicationsoverthelast fewyears.Someofthereasonsofitsrelevancearethereducednumberofparameterstoconfigureand itseaseofimplementation.
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