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  • Introduction Sometimes you may collide with the following problem: a third-party binary component o

    Introduction Sometimes you may collide with the following problem: a third-party binary component or control embedded into your application displays windows (usually message boxes) that hang your code until they are closed. If you do not have the source code of this binary and one does not have a good API to change one’s bad behaviour programmatically, it can be a real headache using the one. A famous example of such a binary is the WebBrowser control in .NET.

    標簽: Introduction third-party Sometimes following

    上傳時間: 2013-12-31

    上傳用戶:pinksun9

  • The following Matlab code converts a Matrix into it a diagonal and off-diagonal component and perfor

    The following Matlab code converts a Matrix into it a diagonal and off-diagonal component and performs up to 100 iterations of the Jacobi method or until 蔚step < 1e-5

    標簽: off-diagonal following and component

    上傳時間: 2013-12-23

    上傳用戶:風之驕子

  • pl0 文法 編譯器 (1)擴充一維整型數組。 擴充var數組:VAR <數組標識名>(<下界>:<上界>)〈下界〉和〈上界〉可用常量標識名。 (2)擴充

    pl0 文法 編譯器 (1)擴充一維整型數組。 擴充var數組:VAR <數組標識名>(<下界>:<上界>)〈下界〉和〈上界〉可用常量標識名。 (2)擴充條件語句的功能使其為:IF<條件>THEN<語句>[ELSE<語句>] (3)增加repeat重復語句: REPEAT<語句>{ <語句>}until<條件>

    標簽: lt gt pl0 VAR

    上傳時間: 2017-07-18

    上傳用戶:R50974

  • data are often used interchangeably, they are actually very different. Data is a set of unrelated in

    data are often used interchangeably, they are actually very different. Data is a set of unrelated information, and as such is of no use until it is properly evaluated. Upon evaluation, once there is some significant relation between data, and they show some relevance, then they are converted into information. Now this same data can be used for different purposes. Thus, till the data convey some information, t

    標簽: interchangeably are different unrelated

    上傳時間: 2017-09-26

    上傳用戶:cxl274287265

  • 數據挖掘-聚類-K-means算法Java實現

    K-Means算法是最古老也是應用最廣泛的聚類算法,它使用質心定義原型,質心是一組點的均值,通常該算法用于n維連續空間中的對象。 K-Means算法流程 step1:選擇K個點作為初始質心 step2:repeat                將每個點指派到最近的質心,形成K個簇                重新計算每個簇的質心             until 質心不在變化  例如下圖的樣本集,初始選擇是三個質心比較集中,但是迭代3次之后,質心趨于穩定,并將樣本集分為3部分    我們對每一個步驟都進行分析 step1:選擇K個點作為初始質心 這一步首先要知道K的值,也就是說K是手動設置的,而不是像EM算法那樣自動聚類成n個簇 其次,如何選擇初始質心      最簡單的方式無異于,隨機選取質心了,然后多次運行,取效果最好的那個結果。這個方法,簡單但不見得有效,有很大的可能是得到局部最優。      另一種復雜的方式是,隨機選取一個質心,然后計算離這個質心最遠的樣本點,對于每個后繼質心都選取已經選取過的質心的最遠點。使用這種方式,可以確保質心是隨機的,并且是散開的。 step2:repeat                將每個點指派到最近的質心,形成K個簇                重新計算每個簇的質心             until 質心不在變化  如何定義最近的概念,對于歐式空間中的點,可以使用歐式空間,對于文檔可以用余弦相似性等等。對于給定的數據,可能適應與多種合適的鄰近性度量。

    標簽: K-means Java 數據挖掘 聚類 算法

    上傳時間: 2018-11-27

    上傳用戶:1159474180

  • JAVA SMPP 源碼

    Introduction jSMPP is a java implementation (SMPP API) of the SMPP protocol (currently supports SMPP v3.4). It provides interfaces to communicate with a Message Center or an ESME (External Short Message Entity) and is able to handle traffic of 3000-5000 messages per second. jSMPP is not a high-level library. People looking for a quick way to get started with SMPP may be better of using an abstraction layer such as the Apache Camel SMPP component: http://camel.apache.org/smpp.html Travis-CI status: History The project started on Google Code: http://code.google.com/p/jsmpp/ It was maintained by uudashr on Github until 2013. It is now a community project maintained at http://jsmpp.org Release procedure mvn deploy -DperformRelease=true -Durl=https://oss.sonatype.org/service/local/staging/deploy/maven2/ -DrepositoryId=sonatype-nexus-staging -Dgpg.passphrase=<yourpassphrase> log in here: https://oss.sonatype.org click the 'Staging Repositories' link select the repository and click close select the repository and click release License Copyright (C) 2007-2013, Nuruddin Ashr uudashr@gmail.com Copyright (C) 2012-2013, Denis Kostousov denis.kostousov@gmail.com Copyright (C) 2014, Daniel Pocock http://danielpocock.com Copyright (C) 2016, Pim Moerenhout pim.moerenhout@gmail.com This project is licensed under the Apache Software License 2.0.

    標簽: JAVA SMPP 源碼

    上傳時間: 2019-01-25

    上傳用戶:dragon_longer

  • Microwave+Line

    Fordecades,microwavelineofsight(LOS)linkshavebeenoneofthebasictechnolo- gies used to build telephone networks. until 1980, the fast rollout of high capacity transport networks and deployment of links in areas with challenging geographic characteristics could not be understood without this technology.

    標簽: Microwave Line

    上傳時間: 2020-05-28

    上傳用戶:shancjb

  • Software+Defined+Radio

    until the mid-1990s most readers would probably not have even come across the term soft- ware defined radio (SDR), let alone had an idea what it referred to. Since then SDR has made the transition from obscurity to mainstream, albeit still with many different understandings of the terms – software radio, software defined radio, software based radio, reconfigurable radio.

    標簽: Software Defined Radio

    上傳時間: 2020-06-01

    上傳用戶:shancjb

  • Optimal+Long-Term+Operation

    This  book  deals with a very important problem in power system planning for countries in which hydrogeneration accounts for the greatest  part  of  the system power production. During the past thirty years many techniques have been developed to cope with the long-term operation  of  hydro reser- voirs. These techniques have been discussed in a number  of  publications, but  they have  not  until now been documented  in  book  form.

    標簽: Long-Term Operation Optimal

    上傳時間: 2020-06-07

    上傳用戶:shancjb

  • Auto-Machine-Learning-Methods-Systems-Challenges

    The past decade has seen an explosion of machine learning research and appli- cations; especially, deep learning methods have enabled key advances in many applicationdomains,suchas computervision,speechprocessing,andgameplaying. However, the performance of many machine learning methods is very sensitive to a plethora of design decisions, which constitutes a considerable barrier for new users. This is particularly true in the booming field of deep learning, where human engineers need to select the right neural architectures, training procedures, regularization methods, and hyperparameters of all of these components in order to make their networks do what they are supposed to do with sufficient performance. This process has to be repeated for every application. Even experts are often left with tedious episodes of trial and error until they identify a good set of choices for a particular dataset.

    標簽: Auto-Machine-Learning-Methods-Sys tems-Challenges

    上傳時間: 2020-06-10

    上傳用戶:shancjb

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