書名：基本商業(yè)程序的建模 Essential Business Process Modeling (Paperback) 作者： Mike Havey 出版商：O Reilly 1 edition (August 18, 2005) 內(nèi)容介紹： Ten years ago, groupware bundled with email and calendar applications helped track the flow of work from person to person within an organization. Workflow in today s enterprise means more MONITORing and orchestrating massive systems. A new technology called Business Process Management, or BPM, helps software architects and developers design, code, run, administer, and MONITOR complex network-based business processes. BPM replaces those sketchy flowchart diagrams that business analysts draw on whiteboards with a precise model that uses standard graphical and XML representations, and an architecture that allows it converse with other services, systems, and users.
標(biāo)簽： Essential Paperback Business Modeling
上傳時(shí)間： 2015-10-21
上傳用戶：zhangyigenius
Sobel--Image Filter (I). An Image filtering is made over data loaded into the on board RAM and presented on a VGA MONITOR.zip
標(biāo)簽： Image filtering Filter loaded
上傳時(shí)間： 2013-12-22
上傳用戶：邶刖
Image_Filter_An_Image_halftone is performed over data loaded into the on board RAM and presented on a VGA MONITOR
標(biāo)簽： Image_Filter_An_Image_halftone performed presented loaded
上傳時(shí)間： 2015-10-27
上傳用戶：cc1915
Magenta Systems Internet Packet MONITORing Components are a set of Delphi components designed to capture and MONITOR internet packets using either raw sockets or the WinPcap device driver. Hardware permitting, ethernet packets may be captured and interpreted, and statistics maintained about the traffic. Uses of packet MONITORing include totalling internet traffic by IP address and service, MONITORing external or internal IP addresses and services accessed, network diagnostics, and many other applications. The component includes two demonstration applications, one that displays raw packets, the other that totals internet traffic. The components include various filters to reduce the number of packets that need to be processed, by allowing specific IP addresses to be ignored, LAN mask to ignore local traffic, and ignore non-IP traffic such as ARP.
標(biāo)簽： Components MONITORing components Internet
上傳時(shí)間： 2015-10-30
上傳用戶：水中浮云
The programming port of all the FP PLC’s support OPEN MEWTOCOL-COM. This is very useful when you want to MONITOR PLC values/bits or to set PLC values or bits via your COMPUTER. You can use any language such as Basic, C, Pascal, Assembler or even if other suppliers of PLCs can send ASCII strings, they can talk to our PLCs to exchange data.
標(biāo)簽： MEWTOCOL-COM programming support useful
上傳時(shí)間： 2015-12-08
上傳用戶：youmo81
This a naive implementation of BOOTP/TFTPBOOT, the protocols to use to bootstrap a computer through a TCP/IP network. The goal was to design a small footprint implementation to allow the code to be integrated into a MONITOR program stored in a ROM/FLASH, the footprint is about 7 KBytes for the test program. This code has not been yet tested in many environment. It should be seen at your starting point to integrate the network boot function to your board.
標(biāo)簽： implementation bootstrap protocols TFTPBOOT
上傳時(shí)間： 2013-12-18
上傳用戶：極客
3、使用如下命令更改密碼： shell> mysqladmin -u root -p password ‘newpass’ Enter Password:******* 出現(xiàn)Enter Password的提示后輸入原來的密碼oldpass即可。 讀者可以嘗試其它所有本章介紹的方法。 4、首先以root用戶的身份連接到服務(wù)器： shell> mysql -u root -p Enter password:******* 出現(xiàn)Enter password提后輸入root用戶的密碼，然后即進(jìn)入mysql客戶機(jī)的交互模式，可以看到下面的提示： Welcome to the MySQL MONITOR. Commands end with or \g. Your MySQL connection id is 4 to server version: 3.23.25-beta-log Type help or \h for help. Type \c to clear the buffer mysql> 然后發(fā)布查詢，直接鍵入題目中的語句： mysql> SELECT User,Host FROM mysql.user
標(biāo)簽： Enter mysqladmin Password password
上傳時(shí)間： 2016-03-17
上傳用戶：talenthn
On-Line MCMC Bayesian Model Selection This demo demonstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to MONITOR the simulation progress (with par.doPlot=1) and modify the simulation parameters.
標(biāo)簽： demonstrates sequential Selection Bayesian
上傳時(shí)間： 2016-04-07
上傳用戶：lindor
In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM algorithm After downloading the file, type "tar -xf EMdemo.tar" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type "EMtremor". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you MONITOR the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets.
標(biāo)簽： Rauch-Tung-Striebel algorithm smoother which
上傳時(shí)間： 2016-04-15
上傳用戶：zhenyushaw
This demo nstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to MONITOR the simulation progress (with par.doPlot=1) and modify the simulation parameters.
標(biāo)簽： sequential reversible algorithm nstrates
上傳時(shí)間： 2014-01-18
上傳用戶：康郎
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