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Description: 基于J2EE技术的快递信息管理系统的研究
结合当前快递业的背景，使用UML技术分析了快递企业对信息技术的各方面
的需求。在此基础上，提出一个以J 2 E E的软件架构为核心，并结合其他信息技术，如
网络技术，终端技术等，设计一套适用于快递业务的信息系统应用模式。整个过程体现
了统一软件开发过程的用况驱动、构架为中心和增量与迭代的特点。快递信息系统采用
R a t i o n a l R o s e来建模，并在J 2 E E服务器We b l o g i c上 部署和实施。
采用 UML建模己经成为面向对象分析和设计的一种趋势，而J 2 E E是一个使开发
多层企业级应用更为简单的统一平台。本论文结合 U ML与M E技术，开发一个实际
的应用系统，对于开发企业级应用有一定的参考价值。
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Author: zhx |
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Description: This a Bayesian ICA algorithm for the linear instantaneous mixing model with additive Gaussian noise [1]. The inference problem is solved by ML-II, i.e. the sources are found by integration over the source posterior and the noise covariance and mixing matrix are found by maximization of the marginal likelihood [1]. The sufficient statistics are estimated by either variational mean field theory with the linear response correction or by adaptive TAP mean field theory [2,3]. The mean field equations are solved by a belief propagation method [4] or sequential iteration. The computational complexity is N M^3, where N is the number of time samples and M the number of sources.
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Author: 陈互 |
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Description: This a Bayesian ICA algorithm for the linear instantaneous mixing model with additive Gaussian noise [1]. The inference problem is solved by ML-II, i.e. the sources are found by integration over the source posterior and the noise covariance and mixing matrix are found by maximization of the marginal likelihood [1]. The sufficient statistics are estimated by either variational mean field theory with the linear response correction or by adaptive TAP mean field theory [2,3]. The mean field equations are solved by a belief propagation method [4] or sequential iteration. The computational complexity is N M^3, where N is the number of time samples and M the number of sources.
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Author: 陈互 |
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Description: ML-EC3使用手册V1[1],明朗产品.-ML-EC3 Manual V1 [1], uncertainty products.
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Author: 吴勇谋 |
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Description: 探讨了在 Mh T I AB环境中实现遗传算法仿真 的方法 ， 并 以一个 简单的求函数最值的问 题作为遗传算法的应用实铡， 说明遗传算法的全局寻优性及用 M AI I AB实现仿真的可行性。-A me f l ~dt o r e Aa z e g e me f i e t I 皿 i n MKI I AB i s d ~- u s s e d．A ha e t i o ~o p t h r f i z a f i o n p r o b l e m i s p r e s e n t e d
t o d l m： l o ml r a t et h e 龉 l y Ⅱ 面 me t h o d 翘 we l l翘 d e m咖曲越i t h e g l o b a l。 n 】 i 越d 衄 f i mc f ima l i t y g e n e t i c~ a g o- r i t h m
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Size: 97280 |
Author: 阿铁 |
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Description: asm编译时的好帮手 不用再繁琐的打开DOS来进行操作 很方便-asm compiler helpful when no longer open a DOS cumbersome to operate easily
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Size: 3214336 |
Author: 要辰 |
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Description: Programa para gravar Chip S3CC921 da impressora ML 1665 com contador de impress鉶
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Size: 209920 |
Author: Ruan Diego |
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Description: reset impressoras samsung scx 4300 4600 4623f 4623fn ml 1640 e 2240
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Size: 11952128 |
Author: dick666dick |
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Description: dump 系统进程信息，结合mimikatz 使用，可以获取系统hash-Using ProcDump
usage: procdump [-a] [[-c|-cl CPU usage] [-u] [-s seconds]] [-n exceeds] [-e [1 [-b]] [-f <filter,...>] [-g] [-h] [-l] [-m|-ml commit usage] [-ma |-mp] [-o] [-p|-pl counter threshold] [-r] [-t] [-d <callback DLL>] [-64] <[-w] <process name or service name or PID> [dump file] |-i <dump file> |-u |-x <dump file> <image file> [arguments] >] [-? [-e]
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Size: 262144 |
Author: shuai |
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Description: LED灯闪烁
- XF.pjt - Debug -
[XF.c] C:\CCStudio_v3.3\C5500\cgtools\bin\cl55 -g -q -fr E:/Debug/Easy5509/EX01_XF/Debug -d _DEBUG -ml -@ Debug.lkf XF.c
[Linking...] C:\CCStudio_v3.3\C5500\cgtools\bin\cl55 -@ Debug.lkf
<Linking>
>> warning: creating output section vectors without SECTIONS specification
>> warning: .sysmem section not found ignoring -heap <size> option.
Build Complete,
0 Errors, 2 Warnings, 0 Remarks.
-LED flashes
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Size: 19456 |
Author: 李强 |
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Description: ML-KNN，这是来自传统的K-近邻（KNN）算法。详细地，为每一个看不见的实例中，首先确定了训练集中的k近邻。之后，基于从标签集获得的统计信息。这些相邻的实例，即属于每个可能类的相邻实例的数量，最大后验（MAP）原理。用于确定不可见实例的标签集。三种不同现实世界中多标签学习问题的实验研究，即酵母基因功能分析、自然场景分类和网页自动分类，表明ML-KNN实现了卓越的性能(ML-KNN which is derived from the traditional K-nearest neighbor (KNN) algorithm. In detail, for each unseen
instance, its K nearest neighbors in the training set are firstly identified. After that, based on statistical information gained from the label sets of
these neighboring instances, i.e. the number of neighboring instances belonging to each possible class, maximum a posteriori (MAP) principle
is utilized to determine the label set for the unseen instance. Experiments on three different real-world multi-label learning problems, i.e. Yeast
gene functional analysis, natural scene classification and automatic web page categorization, show that ML-KNN achieves superior performance
to some well-established multi-label learning algorithms.
2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.)
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Author: 玖
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Description: e Handbook Entry
Communication concepts: Fourier transforms, random signals, Transmitter
and receiver filters, matched filter, Nyquist criterion. Digital Modulation schemes:
M-ary ASK, QPSK, FSK, CPM, spectral analysis of modulated signals, ML and
MAP detectors, signal space methods, bit error rate analysis. Digital Receivers:
carrier and clock synchronisation. Information theory: entropy, channel capacity,
source coding. Channel Coding: block codes, convolutional codes.
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Author: Khan17
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Description: rfer rgge bhjbj jhbj bkl ml tyf e ser y l; ,;l ,; l;ko ihuuyg tyr
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Author: sab75 |
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Description: 用于创建的模型- sfmodel和sfsystem供应的初始值—。用于（1）强调利用电子（样品）的ML估计在expte。（2）如果是测试工作（3）给边缘（后缀），这样用户可以选择创建，比如，z1_mypostfix。(To be used to supply initial values for models created by -sfmodel- and -sfsystem-.
To do:
(1) emphasize the use of e(sample) in ExpTE following the ml estimation.
(2) Test if ctype* work
(3) Give marginal(postfix), so that users can choose to create, say, z1_mypostfix.)
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Size: 17408 |
Author: zwj1121 |
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Description: The cybersecurity community is slowly leveraging machine learning (ML) to
combat ever-evolving threats. One of the biggest drivers for the successful
adoption of these models is how well domain experts and users can under
stand and trust their functionality. Most models are perceived as a black box
despite the growing popularity of machine learning models in cybersecurity
applications (e.g., an intrusion detection system (IDS)). As these black-
box models are employed to make meaningful predictions, the stakeholders’
demand for transparency and explainability increases. Explanations support
ing the output of ML models are crucial in cybersecurity, where experts
require far more information from the model than a simple binary output for
their analysis.
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Author: iqzer0 |
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