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  • 一些數(shù)據(jù)庫(kù)的實(shí)例。共12章。如第八章: 第8章數(shù)據(jù)庫(kù)環(huán)境的建立 1. 用MISDBA用戶登錄MISDB數(shù)據(jù)庫(kù)。 2. 在ISQL中

    一些數(shù)據(jù)庫(kù)的實(shí)例。共12章。如第八章: 第8章數(shù)據(jù)庫(kù)環(huán)境的建立 1. 用MISDBA用戶登錄MISDB數(shù)據(jù)庫(kù)。 2. 在ISQL中,輸入第8章提供的SQL語(yǔ)句;或者根據(jù)表8-1至表8-4在SQL Explorer中自行創(chuàng)建數(shù)據(jù)表。 3. 根據(jù)表8-5至表8-7設(shè)置初始數(shù)據(jù),另外需要在PERSON數(shù)據(jù)表中設(shè)置一個(gè)具有培訓(xùn)管理系統(tǒng)管理權(quán)限的用戶(ID=’PXC’,PASSWD=’PASSWORD’,AUTHORITY=’6’,STATE=’F’)和用于外派培訓(xùn)的用戶(ID=’PXCOUT’,NAME=’外派培訓(xùn)’)。 4. 修改Admin源程序中的數(shù)據(jù)庫(kù)連接屬性,并且重新編譯training.exe。 5. 修改Client源程序中數(shù)據(jù)庫(kù)連接屬性,并且重新生成html文件和cab文件,然后將這兩個(gè)文件拷貝到web服務(wù)器指定目錄中。

    標(biāo)簽: MISDBA MISDB ISQL 數(shù)據(jù)庫(kù)

    上傳時(shí)間: 2014-01-09

    上傳用戶:zxc23456789

  • 1. 用SYSDBA登錄服務(wù)器

    1. 用SYSDBA登錄服務(wù)器,并且創(chuàng)建一個(gè)MISDBA用戶,密碼為PASSWORD。 2. 用SYSDBA用戶創(chuàng)建MISDB數(shù)據(jù)庫(kù)(可直接注冊(cè)使用光盤提供的MISDB.GDB)。 3. 用MISDBA用戶登錄MISDB數(shù)據(jù)庫(kù)。 4. 在ISQL中,依次輸入第5章的數(shù)據(jù)表創(chuàng)建SQL語(yǔ)句;或者根據(jù)表5-1至表5-7自行創(chuàng)建數(shù)據(jù)表。 5. 在SQL Explorer中創(chuàng)建MISDB數(shù)據(jù)庫(kù)連接。 6. 用MISDBA用戶登錄,并且輸入原始數(shù)據(jù)。除了表5-8至表5-11的內(nèi)容,還需要根據(jù)需要設(shè)置部門(DEPARTMENT)、職務(wù)(JOB)和人事科登錄用戶(ID=’RSK’,PASSWD=’RSK’,AUTHORITY=’3’,STATE=’F’)。 7. 修改源程序中的數(shù)據(jù)庫(kù)連接組件參數(shù)。

    標(biāo)簽: SYSDBA 服務(wù)器

    上傳時(shí)間: 2013-12-16

    上傳用戶:縹緲

  • 1. 在IBConsole中添加兩個(gè)用戶LOGIN和MATER

    1. 在IBConsole中添加兩個(gè)用戶LOGIN和MATER,密碼均為PASSWORD。 2. 用MISDBA用戶登錄MISDB數(shù)據(jù)庫(kù)。 3. 在ISQL中,輸入第9章提供的SQL語(yǔ)句;或者根據(jù)表9-1至表9-8在SQL Explorer中自行創(chuàng)建數(shù)據(jù)表。數(shù)據(jù)庫(kù)創(chuàng)建后需要分配LOGIN和MATER用戶的訪問(wèn)權(quán)限。 4. 根據(jù)表9-9和表9-10設(shè)置初始數(shù)據(jù),另外需要在PERSON數(shù)據(jù)表中設(shè)置一個(gè)用于登錄系統(tǒng)的用戶(ID=’MAT’,PASSWD=’PASSWORD’,AUTHORITY=’7’,STATE=’F’),同時(shí)在PART表中添加ID為’0000000000’的零件,名稱為“。 5. 除了修改數(shù)據(jù)庫(kù)連接的屬性,還需要修改數(shù)據(jù)模塊中LOGIN方法的相關(guān)用戶密碼。

    標(biāo)簽: IBConsole LOGIN MATER 用戶

    上傳時(shí)間: 2014-08-06

    上傳用戶:xiaohuanhuan

  • 一個(gè)基于網(wǎng)格和最近鄰居的聚類算法 Similarity(x, y) = size ( SKNN(x) SKNN(y) )

    一個(gè)基于網(wǎng)格和最近鄰居的聚類算法 Similarity(x, y) = size ( SKNN(x) SKNN(y) ),while Link(x, y)=1

    標(biāo)簽: SKNN Similarity size 網(wǎng)格

    上傳時(shí)間: 2014-01-14

    上傳用戶:zhangliming420

  • 這是一個(gè)非常簡(jiǎn)單的遺傳算法源代碼

    這是一個(gè)非常簡(jiǎn)單的遺傳算法源代碼,是由Denis Cormier (North Carolina State University)開發(fā)的,Sita S.Raghavan (University of North Carolina at Charlotte)修正。

    標(biāo)簽: 算法 源代碼

    上傳時(shí)間: 2014-12-05

    上傳用戶:aa17807091

  • DWT變換源代碼

    DWT變換源代碼,As a special exception, you may use this file as part of a free software library without restriction. Specifically, if other files instantiate templates or use macros or inline functions from this file, or you compile this file and link it with other files to produce an executable, this file does not by itself cause the resulting executable to be covered by the GNU General Public License. This exception does not however invalidate any other reasons why the executable file might be covered by the GNU General Public License.

    標(biāo)簽: DWT 變換 源代碼

    上傳時(shí)間: 2014-12-05

    上傳用戶:ynsnjs

  • TI DSP tms320c6416 EDMA測(cè)試代碼

    TI DSP tms320c6416 EDMA測(cè)試代碼,包括QEDMA、EDMA Link功能、Chain功能。這是學(xué)習(xí)EDMA的最生動(dòng)教材。

    標(biāo)簽: c6416 320c 6416 EDMA

    上傳時(shí)間: 2014-01-20

    上傳用戶:sssl

  • ReBEL is a Matlabtoolkit of functions and scripts, designed to facilitate sequential Bayesian infer

    ReBEL is a Matlabtoolkit of functions and scripts, designed to facilitate sequential Bayesian inference (estimation) in general state space models. This software consolidates research on new methods for recursive Bayesian estimation and Kalman filtering by Rudolph van der Merwe and Eric A. Wan. The code is developed and maintained by Rudolph van der Merwe at the OGI School of Science & Engineering at OHSU (Oregon Health & Science University).

    標(biāo)簽: Matlabtoolkit facilitate sequential functions

    上傳時(shí)間: 2015-08-31

    上傳用戶:皇族傳媒

  • Knowledge of the process noise covariance matrix is essential for the application of Kalman filteri

    Knowledge of the process noise covariance matrix is essential for the application of Kalman filtering. However, it is usually a difficult task to obtain an explicit expression of for large time varying systems. This paper looks at an adaptive Kalman filter method for dynamic harmonic state estimation and harmonic injection tracking.

    標(biāo)簽: application covariance Knowledge essential

    上傳時(shí)間: 2014-01-19

    上傳用戶:litianchu

  • This paper deals with the problem of speech enhancement when a corrupted speech signal with an addi

    This paper deals with the problem of speech enhancement when a corrupted speech signal with an additive colored noise is the only information available for processing. Kalman filtering is known as an effective speech enhancement technique, in which speech signal is usually modeled as autoregressive (AR) process and represented in the state-space domain.

    標(biāo)簽: speech with enhancement corrupted

    上傳時(shí)間: 2015-09-07

    上傳用戶:zhangyi99104144

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