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ssc_pneumatic_actuator_MODY
pneumatic model for simulink
- 2019-06-26 12:06:33下载
- 积分：1
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spwm
基于dsp28377的spwm的发生工程文件(SPWM engineering document based on dsp28377)
- 2020-12-01 14:29:26下载
- 积分：1
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简单的通讯录
Simple address book written in c-Simple address book written in c++
- 2023-01-10 22:45:03下载
- 积分：1
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tnstance_RadioButton
Java source code examples, RadioButton instance!
- 2017-05-01 12:20:42下载
- 积分：1
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Multilane_0905-Prescan多车道变道超车场景
说明： 用于Prescan和simulink联合仿真开发多车道变道超车场景(Used for prescan and Simulink joint simulation to develop multi Lane Lane Lane Lane Changing overtaking scene)
- 2020-03-20 08:43:53下载
- 积分：1
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bbs-SNR_Max
说明： 学习盲源分离原理及信号处理，适合初学者对盲源分离算法的学习(Learning Blind Source Separation Principle and Signal Processing, Suitable for beginners to learn Blind Source Separation Algorithms)
- 2020-06-25 07:20:01下载
- 积分：1
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总结—关于VSG惯性
对储能模拟惯性进行了整理分析，可用于逆变器VSG控制(The storage simulation inertia is analyzed and analyzed, which can be used for inverter VSG control.)
- 2020-10-21 20:57:25下载
- 积分：1
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瑞利信道下，OFDM系统中使用BPSK调制的误码率（验证可用，与理论值比较）...
瑞利信道下，OFDM系统中使用BPSK调制的误码率（验证可用，与理论值比较）-BER for BPSK in OFDM with Rayleigh multipath channel
- 2022-07-07 19:02:43下载
- 积分：1
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PCB
说明： ... 超全的AD库 包含了比较全的一些常用库文件,常用51单片机以及C8051F系列，还有一些常用的IC(...(Ultra-wide AD library contains more full of some commonly used libraries, common C8051F series microcontrollers and 51, there are some common IC))
- 2019-02-23 09:40:28下载
- 积分：1
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聚类-k均值算法
说明： K-means算法是基于划分的思想，因此算法易于理解且实现方法简单易行，但需要人工选择初始的聚类数目即算法是带参数的。类的数目确定往往非常复杂和具有不确定性，因此需要专业的知识和行业经验才能较好的确定。而且因为初始聚类中心的选择是随机的，因此会造成部分初始聚类中心相似或者处于数据边缘，造成算法的迭代次数明显增加，甚至会因为个别数据而造成聚类失败的现象。(K-means algorithm is based on the idea of partitioning, so the algorithm is easy to understand and the implementation method is simple and feasible, but it requires manual selection of the initial number of clusters, that is, the algorithm is with parameters. The number of classes is often very complex and uncertain, so professional knowledge and industry experience are needed to better determine. Moreover, because the selection of initial clustering centers is random, some initial clustering centers will be similar or at the edge of data, resulting in a significant increase in the number of iterations of the algorithm, and even the phenomenon of clustering failure due to individual data.)
- 2020-06-21 17:40:01下载
- 积分：1
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