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linux远程连接默认端口port 22
配置文件
在sshd_config里面加入这段
####by Ian#2016-08-07##
Port 52113 #ssh连接默认的端口。因为之前大家都知道是22，所以得修改
PermitRootLogin no #root这个用户大家都知道，所以得禁止它远程登录
PermitEmptyPasswords no #禁止空密码登录
UseDNS no #不使用DNS
GSSAPIAuthentication no
####by Ian#2016-08-07##Innovus 安装全过程
【代码】Innovus 安装全过程。
科普：Frenet坐标
理想情况下，描述这种常见驾驶行为在数学上应该很容易。一种方法是使用新的坐标系。现在，而不是放下一个正常的笛卡尔网格，我们做一些事情，就像你看到下面…Frenet Coordinates是一种以比传统x，y笛卡尔坐标更直观的方式表示道路位置的方式。如果我们想要方程式来描述这个动作，那就不容易了！想象一下像下面这样一条弯曲的道路，笛卡尔坐标系就位于它的上面…使用这些笛卡尔坐标，我们可以尝试描述车辆通常在路上行驶的路径…那么在Frenet坐标系下呈现的典型轨迹是什么样的？在这里，我们定义了一个新的坐标系统。
手把手教你用CCS v7.3调试TI DSP28377：从连接仿真器到看懂Flash里的秘密
本文详细介绍了使用CCS v7.3调试TI DSP28377的全过程，从连接仿真器到解析Flash启动机制。通过实战演示和关键配置要点，帮助工程师快速掌握DSP调试技巧，并深入探讨bootloader设计与在线升级的实现方法，提升开发效率。
ISP Tuning实战：从零开始调校相机传感器的完整流程（附避坑指南）
本文详细介绍了ISP Tuning的完整实战流程，即如何从零开始调校相机传感器以优化成像质量。内容涵盖从黑电平校正、镜头阴影校正等基础标定，到自动曝光、伽马校正、色彩校正等核心画质调校，再到降噪与锐化的平衡策略，并提供了关键的避坑指南。掌握ISP Tuning是提升相机传感器性能、实现从‘能用’到‘好用’的关键技术。
大模型性能压测全指南：从指标到实战
本文系统阐述大模型（LLM/多模态）私有化部署性能压测方案，涵盖核心指标（TTFT、TPOT、吞吐、并发、资源占用、错误率）、四类典型压测场景及工具选型（Locust、vLLM benchmark等）。强调环境隔离、参数固定与长尾延迟分析，提出瓶颈定位思路与标准化报告模板，结合伪代码示例实现流式接口精准打点，确保测试结果真实可信，助力优化模型服务稳定性与性能。
自然语言处理在智能客服系统中的应用
引言与背景 1.1 书籍的目的和内容概述 《自然语言处理在智能客服系统中的应用》旨在探讨自然语言处理（NLP）技术在智能客服系统中的应用，帮助读者理解NLP的核心概念及其在解决实际业务问题中的关键作用。随着人工智能技术的快速发展，自然语言处理成为构建智能客服系统的核心模块之一。本书将深入解析NLP
RK3568平台（KBuild篇）KBuild编译体系
从Linux内核2.6开始，Linux内核的编译采用Kbuild系统，这和过去的编译系统有很大的不同，尤其对于Linux内核模块的编译。在新的系统下，Linux编译系统会两次扫描Linux的Makefile：首先编译系统会读取Linux内核顶层的Makefile，然后根据读到的内容第二次读取Kbuild的Makefile来编译Linux内核。kbuild，即 kernel build，用于编译 Linux 内核文件。
基于STM32F4XX 、HAL库的GXHT30C温湿度传感器驱动应用C语言程序设计
define GXHT30C_STATUS_WRITE_CHK (1 << 0) // 写数据校验状态。#define GXHT30C_STATUS_HUMI_TRACK (1 << 11) // 湿度跟踪报警。#define GXHT30C_STATUS_TEMP_TRACK (1 << 10) // 温度跟踪报警。#define GXHT30C_STATUS_CMD_STATUS (1 << 1) // 命令执行状态。
深度学习工具包 DeepLearningToolbox
深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[](https://bitdeli.com/free "Bitdeli Badge")
WINCC7.3破解文件
WINCC7.3硬件够破解文件，有需要的下载！WINCC7.3硬件够破解文件，有需要的下载！
假人有限元模型
假人模型，车辆碰撞假人有限元模型，适合做乘员安全的研究者利用
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