Accumulated Tech(2)

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#scroll #sqlite #数据库 #command #struct #cocoa

本文汇总了一系列实用的IT技巧和命令，包括数据库备份与还原、时间格式转换、内存使用情况查询等，适用于Linux环境下的开发及运维人员。

一个最好的制作一个数据库档案拷贝的命 令是：

$ echo '.dump' | sqlite3 ex1 | gzip -c >ex1.dump.gz

它产生一个一个名为 ex1.dump.gz的文件，它包含了你以后或在其它机器上重构数据库的所有的信息。要重构数据库，只须敲入：

$ zcat ex1.dump.gz | sqlite3 ex2

需要apache的httpd.conf中文版注释.在硬盘readreview目录中

http://blog.youkuaiyun.com/eroswang/archive/2011/02/24/6205333.aspx

c/c++ 中经常会遇到时间和字符串互相转化的情形

用以下2个函数来转就很方便了

1、时间转字符串函数

size_t strftime( char *strDest, size_t maxsize, const char *format, const struct tm *timeptr );

2、字符串转时间函数

char *strptime(const char *s, const char *format, struct tm *tm);

Linuzx 网站 lxr.linux.no/

* check /proc/{PID}/maps for detail memory usage.

* Coverity in ReadReview archieve folder Is used to find bugs.

* Determine which SQL service pack is installed

http://www.cryer.co.uk/brian/sqlserver/howtowhichservicepack.htm

* 15 examples for Linux top command

http://www.thegeekstuff.com/2010/01/15-practical-unix-linux-top-command-examples/

* VIM

http://blog.youkuaiyun.com/wooin/archive/2007/10/31/1858917.aspx

This is a very useful doc to make vim to be a C/C++ IDE.

* http://product.dangdang.com/product.aspx?product_id=20665125&ref=search-0-mix

苹果开发之Cocoa编程（第三版）

* Visual Studio Find all problems

http://msdn2.microsoft.com/en-us/library/z613zk0e(VS.80).aspx

Control + Scroll Lock fixed the problem

Ctrl + Scroll does not solve the problem neither alt+break...

The reason Ctrl + ScrollLock works

http://blogs.msdn.com/oldnewthing/archive/2008/02/11/7596539.aspx

*

#!/bin/sh

for p in /proc/[0-9]*;

do

cat $p/stat | cut -d" " -f1,2,14,15

done

column 1,2,14 and 15

which is PID, Command, utime and stime.

START=2000

END=3000

for p in /proc/[0-9]*;

do

pid=`basename $p`

# omg no basename? replace with the line below

# pid=`cat $p | cut -d"/" -f3`

if [ $pid -ge $START ]

then

if [ $pid -le $END ]

then

cat $p/stat | cut -d" " -f1,2,14,15

fi

fi

done

* Gnu Global, the programmer’s friend

http://www.gnu.org/software/global/download.html

http://linux.byexamples.com/archives/538/gnu-global-the-programmers-friend/

*vim: Jump to the last known cursor when file open

http://linux.byexamples.com/archives/532/vim-jump-to-the-last-known-cursor-when-file-open/

Add in the setting into your ~/.vimrc

 

if has("autocmd")

  autocmd BufReadPost *

    / if line("'/"") > 1 && line("'/"") <= line("$") |

    /   exe "normal! g`/"" |

    / endif

endif

* xmllint provides you a shell where you can continuously to query your XML.

xmllint --shell myXML.xml

* 查看目标文件信息的命令一般包括：nm、readelf和objdump

objdump -d a.out

readelf -all a.out

readelf --debug-dump a.out | less

表面上最缺的是-金钱

本质上最缺的是-野心

脑袋上最缺的是-观念

对机会最缺的是-了解

命运里最缺的是-选择

骨子里最缺的是-勇气

改变上最缺的是-行动

肚子里最缺的是-知识

事业上最缺的是-毅力

Speed up your PC: Automate your computer maintenance schedule

http://www.microsoft.com/athome/setup/maintenance.aspx?WT.rss_f=At%20Home%20RSS&WT.rss_a=Speed%20up%20your%20PC:%20Automate%20your%20computer%20maintenance%20schedule&WT.rss_ev=a

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翻译：and a material design model, for simulating the characteristics of the semiconductor equipment. Mitrovic and Strang [19] applied the first-principle simulation to virtual sensor measurements to facilitate the process performed by the semiconductor processing tool. However, semiconductor manufacturing consists of hundreds of processing phases, months of processing time and correlative process flows [20]. The corresponding behavior analysis of the tool failure degradation process is very limited. Thus, the results provide neither reliable and accurate physical or mathematical models nor confident experience feedback for mechanism-based modeling. This is the main limitation of the applications of model-based approaches. In the knowledge-based methods, competitive advantages for stabilizing maintenance processes and reducing unplanned costs are realized by holistic consideration of production processes [21]. Prescriptive maintenance is known as the highest maturity and complexity level of knowledge-based maintenance [22], and “how can we control the occurrence of a specific event” should be answered and useful advice for decision-making to be given to improve and optimize the upcoming maintenance processes [23]. Nemeth et al. [22] built on the concept of prescriptive maintenance and proposed a reference model called PriMa-X to support the implementation of a prescriptive maintenance strategy and the assessment of its maturity level, facilitating the integration of data driven methods for predicting future events and identify action fields to reach an enhanced target maturity state. Padovano et al. [24] proposed a framework to construct the missing link between prescriptive maintenance and production planning and control functions in a cyber–physical production environment. Shaheen and Németh [25], Silvestri et al. [26], and Zonta et al. [27] discussed the prescriptive maintenance methods for Industry 4.0 technologies. Obtaining domain knowledge and converting it to precise rules are usually difficult as they require strong background and experience. Besides, the new situations without being covered by the knowledge bases cannot be handled. Data-driven methods are effective when acquiring practical data is more convenient than establishing physical or analytical models [28]. With the development of sensor techniques and wide applications of advanced process control technologies [29], the numerous manufacturing data provide wide opportunities for applying the data-driven prognostics methods to deal with the failure time prediction in the semiconductor manufacturing. Based on the monitoring data, the system degradation tendency can be easily understood and predicted. Generally, data-driven methods applied to the prognostics in the industry are adapted from the ones used in machine learning [29], [30]: neural networks [10], [31], k-nearest neighbor [32], [33], statistical methods [11], [34], decision trees [35], [36], clustering analysis [37], [38], and so on. Regarding the prognostics in semiconductor manufacturing, Bouaziz et al. [40] addressed a predictive approach based on the Bayesian network to analyze the device health factor. It allows early scheduling for preventive maintenance and avoids unscheduled tool downtime. Jia et al. [41] developed an adaptive method on the basis of the polynomial neural network to infer the material removal rate in the chemical-mechanical planarization process of the semiconductor fabrication. 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