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本文是LLM系列文章，针对《Evaluating Self-Generated Documents for Enhancing Retrieval-Augmented Generation with Large Language Models》的翻译。
在检索增强生成系统中，自生成文档 （SGD） 与检索内容的集成已成为提高大型语言模型性能的一种有前途的策略。然而，以前的研究主要集中在优化 SGD 的使用上，而 SGD 的固有特性仍未得到充分探索。因此，本文对不同类型的 SGD 进行了全面分析，并对各种知识密集型任务进行了实验。我们开发了基于系统功能语言学 （SFL） 的 SGD 分类法，以比较不同 SGD 类别的影响。我们的研究结果为哪些类型的 SGD 最有效地有助于提高 LLM 的性能提供了关键见解。结果和基于 SGD 类别的进一步融合方法也为更好地利用 SGD 通过 RAG 实现知识驱动的 QA 任务的重大进步提供了实用指南。
RAG for LLM
船载 OpenHarmony AIS 航海工作台：告警与防碰撞预警系统
船载OpenHarmony AIS航海工作台的告警与防碰撞系统，集成十余类告警（超速、偏航、会遇、SOS等），通过网格空间索引实现高效邻近判定，保障实时性。告警管理器统一调度规则、去重、通知与日志，支持悬浮球、语音、跑马灯、弹窗多渠道触达，确保航行安全。SOS紧急通道独立运行，避免干扰。系统设计强调性能优化与多场景适配，为航海安全提供核心支撑。
AI 应用开发：四维架构落地实战——以 HR Agent 为例拆解感知·推理·记忆·执行【意图路由、规划编排、长期记忆、工具执行、面试题】
本文基于四维架构（感知·推理·记忆·执行），以HR Agent为例，系统拆解大模型应用从“聊天”到“生产可用”的落地路径。通过Spring AI实现意图路由、规划编排、RAG检索与工具调用，结合真实踩坑复盘，揭示架构化设计对安全、成本与可维护性的关键价值，为AI Agent工程化提供可复用的实战框架。
Beyond the Spec: Decoding Samsung‘s LPDDR6 Sub-Channel and Wide-NRZ Innovations
本文深入解析了三星LPDDR6内存的两大核心技术：Sub-Channel架构与Wide-NRZ信号技术。Sub-Channel架构通过将物理通道划分为多个独立子通道，实现了内存访问的细粒度控制，大幅提升了能效，尤其适用于AI与边缘计算场景。Wide-NRZ则通过增加并行数据线而非采用PAM4，在提升带宽的同时保持了优异的信号完整性与低功耗特性。
绕过ROS2！在RDK X5上直接用Python调用海康相机C++ SDK的避坑指南
本文详细介绍了在RDK X5上绕过ROS2，直接使用Python调用海康相机C++ SDK的轻量化方案。通过Cython构建Python绑定，提取海康相机SDK核心组件，实现高效图像采集。文章涵盖环境准备、Cython接口设计、编译系统配置及性能优化技巧，特别适合ARM64平台开发者。
编译原理实验二：LL（1）分析法（附C/C++完整代码）
本文讲述了LL(1)文法的原理及如何用C语言实现。并在给出的代码中实现了消除文法的左递归，First集，Follow集，select集的输出，构造预测分析表并输出，和对任意输入字符串的分析。程序总流程为：从文件读取文法 -> 输出文法信息 -> 消除左递归并输出消除后的文法 -> 计算First集并输出 -> 计算Follow集并输出 ->计算select集并输出 -> 判断是否是LL(1)文法 -> 构造预测分析表并输出 ->手动输入要分析的字符串 -> 输出分析过程和结果读取文件。
rk3566-11.0新增device分区
1.增加块设备 2.修改根目录节点权限 3.开机启动自动挂载 增加块设备 1.修改parameter.txt文件，新增块设备大小及命名 device/rockchip/rk356x/rk3566_r/parameter.txt FIRMWARE_VER: 11.0 MACHINE_MODEL: rk3566_r MACHINE_ID: 007 MANUFACTURER: rockchip MAGIC: 0x5041524B ATAG: 0x00200800 MACHINE
第6篇:ESP32连接无源喇叭播放音乐《涛声依旧》
相邻两个音之间的倍数是2^(1/12)，约等于1.0594630943592952645618252949463）。这样一来，高八度的do频率是低八度do的两倍。我们把频率440赫兹作为中央C ，C调的歌就是把频率440赫兹当作do，乐谱中写作：1=C。一个八度分成12份，八个八度的十二平均音率对应的频率如下（从33Hz到7902Hz）。相邻的两个音之间的差一个半音，12345671之间的间隔分别是“全全半全全全半”。D调的歌就是把从440后面第二个音494赫兹当作do，乐谱中写作1=D。
【AUTOSAR-IpduM】-2.2-创建并配置一个接收RX的(Multiplexed-I-PDU)多路复用
案列背景： 在CFG中配置一个发送TX的(Multiplexed-I-PDU)多路复用Frame。 目录 1 在Arxml中创建一个发送的多路复用Frame 2 CFG导入带多路复用Frame的ARXML 2.1 CFG中提示的错误Error 2.2 如何解决CFG中提示的错误Error 3 结尾 <--返回「Autosar_BSW高阶配置」总目录--> <--返回「Autosar_BSW高阶配置」专栏主页--> <--返回「个人博客」首页--&g..
一文吃透：从零到一开发一套 ABAP RAP 应用的完整路径
ABAP RAP开发实战摘要 本文系统介绍了ABAP RESTful Application Programming Model (RAP)在企业级应用开发中的实施路径。文章以出差申请业务场景为例，详细拆解了RAP开发的六步主线流程： 数据库表设计：规范主键、审计字段设计 CDS数据建模：通过视图定义业务对象实体及关联 行为定义与实现：使用BDEF声明事务规则，Behavior Pool类实现业务逻辑 业务对象投影：裁剪服务接口视图 服务定义与绑定：按OData协议暴露服务 UI集成测试：支持Fiori E
福彩快乐8智能选号系统研究
本文介绍了一款福彩快乐8智能选号系统的开发过程。系统首先通过数据采集获取历史开奖数据，作为分析的基础。随后，对采集的数据进行深入分析，以识别可能的模式和趋势。基于这些分析，系统能够智能预测未来的开奖号码，如2025129期的预测。最后，通过模拟兑奖功能，用户可以直观地看到预测结果的实际效果。文章鼓励感兴趣的读者点赞并领取源代码，以便自行探索和使用该系统。
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")
温度PID控制PLC程序
温度PID稳定控制程序，适用于欧姆龙PLC，对其他PLC也具有很大参考价值
jre9下载 jdk9
(Java SE Runtime Environment 9.0) 9.0u175 官方最新版 【x86|x64】
汉书HE8，好用的汇编代码编写工具
汉书HE8，好用的汇编代码编写工具早期编写汇编代码所用功能强大
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