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在 HarmonyOS 中，RandomAccessFile 是一个用于随机读取和写入文件的类。它提供了对文件的灵活访问，使得我们能够在文件中任意位置读取或写入数据。本文将介绍 RandomAccessFile 类的使用方法，并提供相应的源代码示例。
要使用 RandomAccessFile 类，首先需要创建一个 RandomAccessFile 对象，并指定要操作的文件路径和访问模式（只读或读写）。以下是创建 RandomAccessFile 对象的示例代码：
import java.io.RandomAccessFile;
import java.io【FAQ】从存储权限看HarmonyOS 3.0中应用适配
目前我研究发现3.0.0是适配Android S版本，2.0.0适配的是Android Q版本，大家有兴趣的可以自己研究，欢迎交流。因此需要做相应的适配，查阅官方文档针对Android 11上访问所有文件，有一个新的权限MANAGE_EXTERNAL_STORAGE（如下图中”存储访问权限“开启则可以获取所有外部文件，因而怀疑与3.0系统权限控制有关。”存储“权限变为”媒体和文件“，且只能访问”仅媒体“的文件目录。查阅资料后发现，”仅媒体“权限是Android 11中新增的”媒体文件访问权限“ （
4B认知模型端侧部署实战：轻量化AI的确定性推理之路
认知模型正成为大模型落地的关键演进方向，它区别于传统语言模型，聚焦可验证、可追溯、可审计的结构化决策能力。其核心原理在于分层架构设计（感知-关系-决策）与知识图谱约束下的硬性输出边界，从而在推理链保真度、反事实鲁棒性等关键指标上实现超越通用大模型的确定性表现。技术价值体现在边缘设备上的低延迟、高稳定性与强合规性，尤其适用于医疗初筛、工业故障归因、法律条文引用等强逻辑、低容错场景。本文基于‘明日新程’4B参数认知模型，深入解析其HCP架构、CTS评测体系及硬件感知型端侧部署全流程，为工程师提供从理论到真机落地
3天干完6个月的活,Claude把模型搬上AMD
一条挺刺激的消息：Anthropic 给投资人的材料里有一张幻灯片，写着它的 Claude Mythos Preview 只花了大约，就摸清了怎么让另一个 Claude 模型在 AMD 的 MI355X 上跑起来。同一处还写了个对照数字：过去给一块新芯片做推理适配，通常要几十名工程师、。3 天对 6 个月，这个比值太扎眼，当天中文互联网上就冒出一堆「英伟达的护城河塌了」的说法。我把能翻到的材料都翻了一遍，越看越觉得，塌掉的那一小块是真的，可塌的位置和很多人想的不一样。
高通camx ThreadManager
m_pCore->AcceptNewJob是将一个Job或者任务插入一个Queue，有一个工作线程循环从Queue中取Job任务执行，执行任务就是回调任务中的callback函数，AcceptNewJob实现。调用AddToPriorityQueue将任务添加到queue任务队列，那么什么时候从这个queue中取任务执行呢？有个线程循环执行DoWork，最终调用checkAndDequeue取任务执行。
Jetson Orin边缘语音AI实战：用Riva ASR/TTS + Llama 2打造离线智能语音助手
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别再手动转格式了！用Java+LibreOffice命令行实现Word批量转PDF（附Docker部署避坑指南）
本文详细介绍了如何利用Java和LibreOffice命令行实现企业级Word批量转PDF的自动化解决方案，包括多线程优化、Docker化部署及常见问题排查。通过代码示例和实战经验，帮助开发者高效处理文档转换需求，提升工作效率并避免常见陷阱。
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英飞凌 TC4X MCAL之SPI中断与DMA高效传输实战
本文详细介绍了英飞凌TC4X芯片的SPI通信实战，重点讲解如何通过MCAL配置SPI中断与DMA实现高效数据传输。针对汽车电子领域的高吞吐需求，提供了从基础配置到中断处理、DMA优化的完整解决方案，包含代码示例和调试技巧，帮助开发者提升系统性能与CPU利用率。
基于YOLOv8的包装商品智能检测系统完整指南—含论文、代码
《基于YOLOv8的包装商品智能检测系统》摘要： 本文提出了一套完整的零售商品自动检测解决方案，利用YOLOv8深度学习框架实现高精度、高效率的商品识别。系统针对SKU110K大规模商品数据集（含11,762张高密度货架图像）进行优化，包含数据预处理、模型训练、推理检测和可视化界面全流程。详细介绍了从CSV标注到YOLO格式的转换方法、环境配置指南及技术架构设计。该系统可应用于智能零售、物流仓储等多个场景，有效解决人工盘点效率低、误差大的痛点。实验表明，该系统具备毫秒级检测速度和批量处理能力，为商业数字化转
24、语料库与科技术语库建设
本文探讨了语料库在科技术语库建设中的关键作用，重点分析了词性标注、索引、搭配、词形还原、频率计数、词汇排序和类型-标记分析等核心技术的应用与挑战。文章以印度语言为例，指出其语料库处理工具匮乏的现状，强调需开发适配本地语言特征的技术，并提出一个整合多种处理步骤的综合流程，旨在推动印度语言科技术语数据库的系统化建设，促进科技语言的发展与应用。
6、基于深度强化学习的无人机路径规划
本文研究了基于深度强化学习的无人机路径规划方法，提出了一种融合改进DQN算法与人工势场法的路径规划策略。通过设计合理的网络结构、状态空间、动作空间和奖励函数，结合ε-贪心策略进行动作选择，实现了无人机在复杂环境中的自主避障与目标导向飞行。仿真实验表明，该方法具有收敛速度快、实时性强和可操作性高等优点，能有效完成室内环境下的路径规划任务。文章还总结了算法优势，并对未来在连续动作、复杂环境适应及多无人机协同等方面的发展方向进行了展望。
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")
精雕软件(JDpaint) 5.5 绿色版
精雕软件(JDpaint) 5.5 绿色版精雕软件(JDpaint) 5.5 绿色版精雕软件(JDpaint) 5.5 绿色版
jdk-8u144-linux-x64.tar.gz.
该资源为搭建Linux系统Java环境用，资源的后缀为tar.gz格式的压缩包
IEM模型，地表粗糙度模拟
利用微波反演土壤水分时候，利用IEM模型可以实现对随机地表的模拟
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