Accumulated tech

最新推荐文章于 2025-12-09 16:47:37 发布
原创 最新推荐文章于 2025-12-09 16:47:37 发布 · 259 阅读 · 0 ·
CC 4.0 BY-SA版权
版权声明：本文为博主原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接和本声明。
文章标签：

#training #leak #python #performance #video #access

Part  1: Pure Tech

1.  Useful Linux tricks by step-by-step examples

          including shell/python/administration:  http://linux.byexamples.com/

2 100+ best free SW

which spread in all kinds of fields to easy our life, some of them should be very familiar to you, and some can give you some hints if you are seeking some candidates for work or project integration.

Check it at http://www.pcmag.com/article2/0,2817,2260070,00.asp

http://www.channelinsider.com/c/a/Spotlight/25-Free-Software-Programs-Worth-the-Download-691265/?kc=EWKNLEDP06252010A

4 Python Fundamentals video training

· Python Fundamentals (Video Training) 1 :http://www.eweek.com/c/a/Video/Python-Fundamentals-Video-Training-1/

· Python Fundamentals (Video Training) 2 :http://www.eweek.com/c/a/Video/Python-Fundamentals-Video-Training-2/

· Python Fundamentals (Video Training) 3 :http://www.eweek.com/c/a/Video/Python-Fundamentals-Video-Training-3/

· Python Fundamentals (Video Training) 4 :http://www.eweek.com/c/a/Video/Python-Fundamentals-Video-Training-4/

· Python Fundamentals (Video Training) 5 :http://www.eweek.com/c/a/Video/Python-Fundamentals-Video-Training-5/

· Python Fundamentals (Video Training) 6 :http://www.eweek.com/c/a/Video/Python-Fundamentals-Video-Training-6/

  9 Free Python Books:

http://linuxtoy.org/archives/9-free-python-books.html

4 Free Perl Books:

http://linuxtoy.org/archives/4-free-perl-books.html

5. a tool to check stack overflow

In Linux: Valgrind

For mac: : http://www.macwrite.com/critical-mass/memory-leaks-mac-os-x

*. Mac CrashReporter

   Can catch SIGBUS and SIGSEGV, and log is at ~/Library/Logs/CrashReporter/.

* Memory leak on windows

   DevPartner Studio Professional is a suite of tools allowing a developer to analyze .NET code for

    * Code Quality and Complexity

    * Memory Leak Detection

    * Memory Optimization

    * Performance Analysis (Timing)

    * Performance Expert (CPU, Disk and Network resource usage)

    * Code Coverage Analysis

    * Fault Simulation (both .NET and environmental)

    * Error Detection and Interop monitoring for C/C++ using BoundsChecker technology.

* Mac Daemons

On Mac OS X, the engine will be launched as root by launchd. As a daemon, it can only access a small set of frameworks, but we get enough of them to do what we need to do.
In specific answer to the question, the yeti engine will be able to mount volumes; shared or local.
See the following technical note for more information about daemons.
http://developer.apple.com/technotes/tn2005/tn2083.html#SECLIVINGDANGEROUSLY

* Mac port leak

mach ports are part of the mach IPC mechanism. They are similar to pipes. See the below link for more information.
http://developer.apple.com/documentation/Darwin/Conceptual/KernelProgramming/boundaries/chapter_14_section_4.html
I found this leak by watching the output from ‘top’. The ‘#PRTS’ column shows you how many ports a process has open. A port leak is equivalent to a memory leak since applications have only a limited number or ports available to use.
I tracked down the current leak to a scsi device access method called ObtainExclusiveAccess that we use to poll for device availability. Charles is trying to figure out now if we are using it wrong or if this is something we will have to work around.

*. Screen Sharing

      From: http://www.macworld.com/article/131094/2007/12/screensharepower.html

      Type the following into a terminal window to turn on screensharing via Bonjour:

      defaults write com.apple.ScreenSharing ShowBonjourBrowser_Debug 1

      If you don't know what screensharing is, read the article.

9 Wcsrtombs     UTF字符转换

10 inotify

            Inotify-tools Project website:

http://wiki.github.com/rvoicilas/inotify-tools/

            Inotify-tools help:

http://hi.baidu.com/all_i_have_wan/blog/item/039aa161ac867348eaf8f8e7.html

            Just now, I used the following command:

            Inotifywait –mr –e close_write /dir

11 Linux Kernel picture

      Pretty cool page with a lot of useful links:

http://www.makelinux.net/kernel_map.shtml

      i.e., an old but good article on the VMM:

http://www.redhat.com/magazine/001nov04/features/vm/

* Linux documentation

http://lxr.linux.no/

* CVS exclude tar

      tar -cvf backup.tar --newer 'Apr 12 13:15 2008' --exclude-from=exPattern --exclude-vcs *

* mstsc –v:servername /console

            log on to a Windows Server 2003-based server that is running Terminal Services remotely and interact with session 0 as if you were sitting at the physical console of the computer

            More information about mstsc command, please refer to http://support.microsoft.com/kb/278845

* function UWow64EnableWow64FsRedirection(bool).

        In 64-bit windows, there are two system foler.System32 is for native access, and it is for 64-bit application. The other is SysWoW64, which is for 32-bit application.

        If we call this function passing argument true, system will redirect the access to system32 folder to SysWoW64 for 32-bit application.

                This function is based on thread. That is to say enabling/disabling redirction only has an effect on the current thread. According to MSDN, enabling direction is the default. Only if one does want to access the 64bit  system32 folder, He/she should disabling the redirection. I cannot know why our code disables the redirection. I search this function in our solution, and find as many as 48 callings. Our code seems to disable the redirection in advance. Only we need redirection, we enable it.

* Setup NFS

/home/pan/shared 192.168.0.6/255.255.255.0(rw,insecure,no_root_squash,async,no_subtree_check)

* Component identity found in manifest does not match the identity of the component requested.

All DLLs can be found in VS2005's installed folder: Microsoft Visual Studio 8/VC/redist.

If it’s debug version, copy the dlls in the debug folder to your app folder.One example is:

/Microsoft Visual Studio 8/VC/redist/Debug_NonRedist/amd64/Microsoft.VC80.DebugCRT

* Mac boot setting

file /Library/Preferences/SystemConfiguration/com.apple.Boot.plist

* Windows Performance tools within Visual Studio 2005

http://msdn.microsoft.com/en-us/library/z9z62c29%28v=VS.80%29.aspx

zchenhongqiang
关注
Accumulated Tech(2)
03-22 192
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/archi
技术债务管理_管理技术债务
weixin_26717681的博客
08-15 1799
技术债务管理DevOps Essentials DevOps基础 介绍 (Introduction) Technical debt is one of the most insidious and damaging phenomena in software development. If left unchecked, it can destroy projects, products, e...
Why I Quit My Job to Start a Tech Company
AJIOY的专栏
08-31 1708
Author:Vinicius Vacanti August 3, 2011 Vinicius Vacanti is co-founder and CEO of Yipit, a service that finds you great local deals by learning your tastes. Here he shares the lessons he learn
03-Elastic日志系统-filebeat-kafka-logstash-elasticsearch-kibana-6.8.0搭建流程
zhangpfly的博客
01-19 3904
03-Elastic日志系统-filebeat-kafka-logstash-elasticsearch-kibana-6.8.0搭建流程1. 介绍2. 准备工作2.1 软件版本2.2 日志流3. 配置zookeeper集群 1. 介绍 前面写了使用redis作为中间缓存，消息队列，进行日志流的削峰，但是在使用的过程中，前期日志量不大的情况下，完全没有问题，当日志不断上升，redis队列不断堆积的...
20世纪物理学：对宇宙认知的一次巨大飞跃
howard2005的专栏
01-30 1737
20世纪的现代物理学见证了人类认知宇宙本质的一次伟大飞跃，物理学家们的开创性工作不仅深刻影响了基础科学的发展方向，也成为了现代社会科技进步与文明进程的重要推动力。随着科学技术的持续进步，我们期待未来能够揭开更多关于自然界的奥秘。
大学英语综合教程三 Unit 1至Unit 8 课文内容英译中 中英翻译
热门推荐
亓官劼的博客
05-01 14万+
大学英语综合教程三 Unit 1至Unit 8课文内容英译中 中英翻译   大家好，我叫亓官劼（qí guān jié ），在优快云中记录学习的点滴历程，时光荏苒，未来可期，加油~博客地址为：亓官劼的博客 本文原创为亓官劼，请大家支持原创，部分平台一直在盗取博主的文章！！！ 博主目前仅在优快云中写博客，唯一博客更新的地址为：亓官劼的博客 文本为博主整理翻译所得，送给有...
Martin Odersky Scala编程公开课 第三周作业
Focustc
10-09 7092
Functional Programming Principles in Scala  by Martin Odersky 这次的作业叫做Object-Oriented Sets。要完成一个完整的类，实现取最大值、排序等方法。由于是函数式编程，这些的实现方法和以往我知道的完全不一样。 总结 TweetSet有两个子类，Empty和NonEmpty，使用BFT实现。有一点比较惊奇的是
高效技术领导的5个锦囊妙计
程序•袁
03-31 1053
Becoming a Tech Lead is a tough transition for any developer, because only part of the skills and experience you had as a developer prepares you for the expectations of a new role. Instead of simply de
常用英语缩写
weixin_46244732的博客
03-17 2万+
Abandon简写为ABAN Abandoned简写为ABD Abbreviate简写为ABBR Abbreviated简写为ABR Abbreviation简写为ABR Ability简写为ABL Abundance简写为ABUND Accelerate简写为ACLR Accelerated简写为ACLR Acceleration简写为ACLR Accept简写为ACPT Acceptability简写为ACPT Acceptance简写为ACPT Accepted简写为ACPT Access简写为ACS
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. Yang and Lee [42] applied the Bayesian belief network to analyze the causal relationship of the process variables and estimate their effects on wafer quality to achieve high-classification rates for wafer quality and identify the problematic sensors when any bad wafer is detected. In reality, the quantity and quality of the data collected from the processes are problematic when it comes to developing and maintaining effective data-driven prognostics models [43]. As the failure can be slowly accumulated, the relevant data for evaluating the failure behavior would be scarce. In addition, most data-driven prognostics methods in semiconductor manufacturing focus on the deterministic estimation without giving the confidence level of the inference of the tool failure prognostics, which implies how much effect on the accuracy of the prognostic models built upon the acquired data is unknown. Using the poorly monitoring data would result in an improper estimation of the cleaning time. For more accurate prognostics, it is necessary to have a good and reliable prognostic model. As the tool failure mechanisms and uncertainty in semiconductor manufacturing are complex and unavailable, this article proposes a data-driven prognostic method to study the failure behavior and predict the tendency on the basis of the obtained production data in the fabrication process. One of the achievements is implementing the prognostics for determining the explicit cleaning time and reducing the dependence on the mechanism analysis. A novel failure factor extraction and prognostic model is developed based on autoassociative regression (AR)-Gaussian process (GP), which is the integration of the AR [44] and the GP [45]. It can be used to analyze and extract the failure factors by the comparisons between the actual data and the fault-free data. Furthermore, with the analysis of the residual tendency, the abnormalities can be detected to be used as the variables of the prognostic model. To infer the future failure behaviors in the fabrication process of semiconductor manufacturing, the data-driven prognostic model is constructed on the basis of GP to present the degradation evolution in a probability distribution and evaluate the uncertainty. Thanks to the confident interval of each estimated value, the accuracy of the prediction influenced by the collected data quality can be presented. It cannot be done by the deterministic estimation. And the suitable cleaning time for the fabrication process is given based on the discussion of the possible failure phenomena in semiconductor manufacturing. This article is structured as follows. The prognostic problem in semiconductor manufacturing is defined and its issues are discussed in Section II. Section III details the novel AR-GP method. The feasibility and the promising results of the ARGP method are validated through a numerical example and a practical semiconductor manufacturing process for the failure factor extraction and failure behavior inference, respectively, in Section IV. Finally, Section V draws conclusions.
11-24
半导体制造中的故障预测方法可分为三类，现就关键内容进行翻译和分析： 1. **模型驱动方法的局限**： - 现有模型（如Takako的3D设备仿真模型或Mitrovic的第一性原理模拟）难以覆盖数百道制程环节和长达数月的生产...
Learn Electronics with Arduino
05-22
Have you accumulated a junk box of electronic parts and are now wondering what to build? Learn Electronics with Arduino will answer these questions to discovering cool and innovative applications ...
20、Cybersecurity Experts: Profiles and Insights
apple5的专栏
11-13 38
This blog introduces a diverse group of cybersecurity professionals from around the world, highlighting their unique career paths, areas of expertise, and contributions to the field. From security architects and CISOs to advocates for diversity and educati
50、【Ubuntu】【Gitlab】拉出内网 Web 服务：http.server 单/多线程分析（二）
HIT_Weston的博客
12-04 1428
本文分析了Python的http.server模块在单线程和多线程模式下的性能表现。通过测试发现，单线程模式下（Python<3.7）并发请求会被阻塞，后发请求需要等待前一个请求完成；而多线程模式下（Python≥3.7）可以同时处理多个请求。作者通过slow_server.py脚本模拟耗时操作，使用time curl命令测试响应时间，验证了线程模型的差异。文章还指出Python 3.7是一个重要分水岭，官方将默认命令行服务器升级为多线程以提升用户体验。技术细节参考了CPython源码，并提供了Git
深度学习下载包时可能会遇到的问题及解决方案
最新发布
m0_50481455的博客
12-09 242
若确实下载安装了CUDA ，但是此时输出的CUDA是否可用为否，应该是torch的版本为cpu版本导致，刚刚的下载包的语句如果总是下载的是cpu版本，我们考虑直接去网站下指定包，再进行安装。CUDA Version表示的是驱动支持的最高 CUDA 版本，去官网下载 CUDA ，我这里是12.2，表示下载的版本最大只能是12.2。然后下载包时，比如本地环境是Python3.9，找包下载时候，3.9要下对应cp39的包。下载好后，执行语句安装。
推荐 | JoyAgent-JDGenie：开箱即用的端到端多智能体产品
lpfasd123的博客
12-05 362
如果你在寻找一款真正可落地的多智能体产品，用来“搜索-分析-生成报告”、“数据问答与诊断”、“代码解释与图表生成”，同时希望易部署、易扩展、易二次开发——JoyAgent-JDGenie 是非常值得试用与推荐的选择。只需填好少量配置，即可获得端到端的流式体验与交付能力。
零基础学JAVA--Day41(IO文件流+IO流原理+InputStream+OutputStream)
Dxxyyyy的博客
12-05 979
文件在程序中是以流的形式来操作的流：数据在数据源（文件）和程序（内存）之间经历的路径输入流：数据从数据源（文件）到程序（内存）的路径输出流：数据从程序（内存）到数据源（文件）的路径。
Python 海象运算符
这是一个c++热爱者的博客哟
12-08 488
Python 3.8引入的海象运算符(:=)允许在表达式中进行变量赋值，能有效减少重复代码。它特别适用于循环条件、列表推导式等场景，如while (line := file.readline()):可简化文件读取操作。使用时需注意：必须加括号，避免在复杂表达式中过度使用以免降低可读性。虽然该特性能精简代码，但应遵循团队约定，在保持代码清晰的前提下合理使用。
第30篇：逆袭量化路：用 bot_start 和 bot_loop_start 玩转 Freqtrade 策略
qq_36936892的博客
12-06 284
Freqtrade策略开发中，bot_start()和bot_loop_start()是两个关键生命周期函数。bot_start()在机器人启动时仅调用一次，适合初始化数据、加载资源等操作；bot_loop_start()在每个交易循环开始时触发，可用于状态刷新、动态参数调整等周期性任务。开发者应注意前者只需快速执行，后者需保持轻量以避免阻塞。合理使用这两个函数能增强策略灵活性，支持复杂交易逻辑实现。掌握这两个回调函数是提升Freqtrade策略开发效率的重要环节。
python+django/flask+vue的基于协同过滤算法的体育商品推荐系统
Q_Q1963288475的博客
12-04 659
相比于传统的体育商品推荐方式，个性化智能的管理方式可以大幅提高体育商品推荐的管理效率，实现了个性化智能体育商品推荐的标准化、制度化、程序化的管理，有效地防止了体育商品推荐的随意管理， 功能主要包括首页、个人中心、用户管理、商品分类管理、商品信息管理、交流论坛、留言板、系统管理、订单管理等功能，从而实现个性化智能体育商品推荐方式，提高个性化智能体育商品推荐的效率。
Accumulated Polar Feature-based
03-08
### Accumulated Polar Feature-Based Method in Computer Vision The accumulated polar feature (APF)-based method is a robust technique used primarily within the domain of computer vision and image processing to analyze, recognize patterns, and extract features from images. This approach leverages the transformation of Cartesian coordinates into polar coordinates around key points identified within an image. #### Key Concepts Transforming data into polar space allows for more effective representation of circular or radial structures present in visual content. The accumulation process involves aggregating information over specific angular sectors at varying radii from selected keypoints. By doing so, this method can capture both local texture details as well as broader structural characteristics simultaneously[^1]. #### Implementation Steps To implement APF-based methods effectively: - **Keypoint Detection**: Identify distinctive regions across different scales using algorithms like SIFT or FAST. - **Polar Transformation**: Convert neighborhoods surrounding these keypoints into polar coordinate systems where each point has distance \( r \) and angle \( θ \). - **Feature Extraction**: Aggregate intensity values along concentric circles centered on detected keypoints while dividing them into multiple bins based upon angles. Here's how one might code such functionality in Python with OpenCV library support: ```python import cv2 import numpy as np def compute_polar_features(image, kp): # Extract patches around keypoints patch_size = 32 winSize = (patch_size, patch_size) descriptors = [] for p in kp: x, y = int(p.pt[0]), int(p.pt[1]) # Crop region of interest roi = image[y-patch_size//2:y+patch_size//2, x-patch_size//2:x+patch_size//2] # Compute gradient magnitude and orientation mag, ang = cv2.cartToPolar(cv2.Sobel(roi,cv2.CV_64F,1,0,ksize=5), cv2.Sobel(roi,cv2.CV_64F,0,1,ksize=5)) # Create histogram per sector hist = np.zeros((8,)) bin_edges = np.linspace(-np.pi,np.pi,9) for i in range(len(mag)): idx = np.digitize(ang[i],bin_edges,right=True)-1 if(idx>=0): hist[idx%8]+=mag[i] descriptors.append(hist.flatten()) return np.array(descriptors).astype(np.float32) img = cv2.imread('example.jpg',cv2.IMREAD_GRAYSCALE) detector = cv2.FastFeatureDetector_create() keypoints = detector.detect(img,None) descriptors = compute_polar_features(img,keypoints[:10]) # Limit number of keypoints processed here print(f"Descriptors shape: {descriptors.shape}") ``` This script demonstrates extracting simple histograms representing distribution of gradients' orientations inside patches defined by Fast corners found earlier in grayscale input imagery.
评论
添加红包

请填写红包祝福语或标题

个

红包个数最小为10个

元

红包金额最低5元

当前余额3.43元 前往充值 >
需支付：10.00元
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付元
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明：

1.余额是钱包充值的虚拟货币，按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载，可以购买VIP、付费专栏及课程。

余额充值