记录一次StackOverflowError问题

如何使用Calibre PERC抽出 点到点/p2p 电阻 @[TOC](如何使用Calibre PERC抽点到点(p2p)电阻) 一、前言 需要实现的功能是从gds中抽出net的点到点的电阻。如下图这样。 二、准备工作 lvs rule pex rule(必要时可以自己定义RC rule) gds p2p点的坐标 定义prode.defs 因为p2p的点是变化的,所以每次prode定义都要重新出,对应的perc rule也是根据每个p2p点的定义来的,perc rule也是每次都要重新出。如果需要可以写一个脚本自动出这些定义文件。 PROBE_POINTS 阅读详情

StackOverflowError

Idea启动一直抛出StackOverflowError栈溢出,大概率是跟刚写的代码相关

public class WebServiceConfig {

    private static WebServiceConfig cfg = new WebServiceConfig();

    // Web服务的URL
    private String address;

    // Web服务接口
    private Class serviceClass;

一开始配置对象是加了@Data注解的,toString方法导致的StackOverflowError栈溢出

【GlobalMapper精品教程】084:tif(tfw)、jpg(jgw)、png(pgw)的区别与联系 摘要:本文介绍了TFW、JGW、PGW等地理参考文件的基本原理和应用。这些文件通过简单的文本格式存储栅格图像的地理坐标信息,适用于平面坐标系下的GIS数据处理。文章详细说明了文件各行的含义,并提供了在ArcGIS和Globalmapper中生成这些文件的具体操作步骤,包括导出TIFF、JPG、PNG格式时勾选生成对应坐标文件的选项。这些文件虽然简单易用,但仅适用于平面坐标系,且功能相对有限。配套视频教程提供了更详细的操作演示。 阅读详情

相关推荐

手把手教你用nRF52832+RFX2401C设计远距离蓝牙模块(附完整原理图/PCB文件)

本文详细介绍了如何利用nRF52832 SoC与RFX2401C射频前端芯片设计远距离蓝牙模块,涵盖芯片选型、阻抗匹配、PCB布局等关键环节。通过实战指南和完整原理图/PCB文件,帮助工程师实现输出功率可达20dBm的蓝牙模块设计,适用于智能家居、运动追踪等物联网应用。

weixin_29214199的博客 286

java webservice md5加密_MD5加密出现java.lang.StackOverflowError: stack size 1036KB错

先上代码:AlertDialogchangePwdDialog;EditTextetOldPwd,etNewPwd,etNewPwdSure;privatevoidshowChangePWDDialog(){ViewchangePwdEdit=LayoutInflater.from(mContext).inflate(R.layout.dialog_change_password...

weixin_42362931的博客 461

AVL(Athena Vortex Lattice)如何编写与应用(一)

前两个是弦向的涡个数和以cos函数分布的间距参数(设为1即可)后两个可以在这里声明也可以写到后面的每一个section中,是展向的涡个数和以sin函数分布的间距参数。这个文件是用来建立飞机模型的文件,是一定要有的文件,其他的.run和.mass并不是必须要有的,甚至mass文件并没有看到有人在使用。Sref、Cref、Bref是飞机的参考面积参考弦长和参考的展长,我的理解就是所设计飞机的S,c,b这三个参数。———————————————以上是飞机的基本信息———————————————

dawangaa的博客 1508

Tomcat 7.0.3x 启动时遇到StackOverflowError导致失败的解决办法

Tomcat 7.0.3x 启动慢并且遇到StackOverflowError的异常的解决办法 之前使用tomcat7时遇到启动报错问题,日志如下: Nov 07, 2014 9:52:10 AM org.apache.catalina.core.AprLifecycleListener init INFO: The APR based Apache Tomcat Native librar

Jeffrey's 专栏 9230

TMC22系列步进电机驱动设计:从静音原理到硬件实战

步进电机驱动是运动控制系统的核心,其性能直接影响设备的精度、噪音和效率。传统驱动方案如A4988采用方波控制,存在转矩脉动大、运行噪音高等问题。而高性能驱动通过优化电流波形与切换策略,从根本上提升了运动质量。其中,**StealthChop2**技术通过同步MOSFET开关与电机反电动势过零点,实现了近乎无声的低速运行;**SpreadCycle**模式则通过逼近正弦波的电流调节,优化了中高速动态性能。这些技术为3D打印机、CNC机床、自动化设备等对静音和精度有要求的场景提供了关键解决方案。本文以广泛应用的

weixin_31858171的博客 353

汽水音乐自动化薅羊毛教程:从零搭建稳定赚钱脚本

📖 前言:为什么要学习自动化脚本?在数字时代,时间就是金钱。汽水音乐等App通过观看广告奖励用户金币的模式,本质上是将我们的时间转化为收益。本教程将教你如何用Python自动化这个过程,让程序为你"打工",实现被动收入。通过本教程,你将学到:移动端自动化测试的核心技术Python在自动化领域的实际应用如何将重复性任务转化为自动化流程创造属于自己的"数字员工"第一章:环境准备与基础配置1.1 硬件要求Android手机一部(建议使用备用机)USB数据线。

weixin_44947827的博客 5759

记录一次jar包冲突导致的StackOverflowError,问题排查和解决。

tomcat项目启动报错: 严重: A child container failed during start java.util.concurrent.ExecutionException: org.apache.catalina.LifecycleException: Failed to start component [StandardEngine[Tomcat].StandardHost[localhost].StandardContext[]] at java.util.concurrent

糯米爱吃西瓜 788

信噪比(Signal-to-Noise Ratio, SNR)详细介绍

信噪比(Signal-to-Noise Ratio,SNR)是衡量信号质量的重要参数,表示有用信号的功率与背景噪声功率的比值。其中,\( P_{\text{signal}} \) 表示信号功率,\( P_{\text{noise}} \) 表示噪声功率。- 音频处理:在音频设备和录音系统中,SNR决定了音质的好坏,高SNR表示音频信号更清晰,噪声更少。- 低SNR:信号中噪声成分较大,接收端难以分辨有用信号和噪声,可能导致误码率高,通信质量差。SNR定义为信号功率与噪声功率之比,通常用分贝(dB)表示。

qq_58860480的博客 8万+

一个常见的StackOverflowError问题

在开发中遇到一个StackOverflowError问题,感觉挺开心,就记录一下吧。报错日志如下: com.fasterxml.jackson.databind.JsonMappingException: Infinite recursion (StackOverflowError) (through reference chain: org.xnio.nio.NioXnioWorker["i...

成の博客 1988

MultiOTP Credential Provider避坑指南:从安装到RDP双因素认证的全流程解析

本文详细解析了MultiOTP Credential Provider从安装到实现RDP双因素认证(MFA)的全流程,重点分享了Docker部署、Active Directory集成及网络级认证(NLA)兼容性等环节的实战避坑经验,旨在帮助运维人员高效、稳定地部署离线双因素认证方案,提升Windows远程桌面安全性。

weixin_29255731的博客 640

H3C BGP联盟实验

bgp,bgp联盟,bgp公认团体属性,

一只懒羊羊 968

dubbo调用发生StackOverflowError栈溢出异常

昨天在工作中,dubbo抛出了一个bug——java.lang.StackOverflowError: null,具体的报错信息如下: Caused by: java.lang.StackOverflowError: null at com.alibaba.com.caucho.hessian.io.Hessian2Output.writeObject(Hessian2Output.java...

RicardoDing的博客 2866

uniapp+google map不显示

uniapp+renderJS+google map开发安卓版APP非小程序,google map小叉号不显示,google map自定义marker右上角的小叉号如何去掉,uniapp+google map不显示

renkai721的专栏 525

记录一次学习过程中遇到的StackOverflowError

这里写自定义目录标题记录一次学习过程中遇到的StackOverflowError栈的特点功能快捷键合理的创建标题,有助于目录的生成如何改变文本的样式插入链接与图片如何插入一段漂亮的代码片生成一个适合你的列表创建一个表格设定内容居中、居左、居右SmartyPants创建一个自定义列表如何创建一个注脚注释也是必不可少的KaTeX数学公式新的甘特图功能,丰富你的文章UML 图表FLowchart流程图导出与导入导出导入 记录一次学习过程中遇到的StackOverflowError StackOverflowErr

qq_41491367的博客 314

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');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

RU DOS版、EFI版32bit/64bit BIOS常用工具

DOS版、EFI版的RU工具,可用来查看IO、MEM、PCI、SMBIOS等资源。亲测在EFI SHELL下可用。

记一次线上启动war包 StackOverflowError

背景:一次线上部署应用,启动还未进入spring初始化阶段、直接报错 吓的我一哆嗦 六月 18, 2020 8:36:14 下午 org.apache.catalina.core.ContainerBase addChildInternal 严重: ContainerBase.addChild: start: org.apache.catalina.LifecycleException: Failed to start component [StandardEngine[Catalina].Standar

e_shi_yi_p的博客 298

Outlook Express 6 (OE6) for Win7/Win8/Win10

英文版 Outlook Express6.0, 兼容Win7/Win10/WinXP,解压就可用。

java.lang.StackOverflowError

今天碰到一个错误,一个理所应当的错误,特来记一下.以后碰到就能秒解决了 异常类型: java.lang.StackOverflowError 错误代码 List> resultList = deliveryOperateMapper.searchBillOfdelivery(entity); List list =new ArrayList<>(); HashMap

alan_waker的博客 2410

锂电池充电器电路设计 里面有电路

锂电池充电器电路设计 里面有电路 3.7V升压到5V电路设计。

记一次spark mllib stackoverflow踩坑

以前做als相关的东西的时候,都是用的公司的内部工具居多,今天第一次用了下spark的mlib,拿了个几M的小数据集试了个水。。 结果一跑,我擦。。。居然stackoverflow了。。 源码如下: from pyspark.mllib.recommendation import ALS from numpy import array from pyspark import Spar

黑暗之神 3374

1、记一次将applicationContext对象注入到容器中导致java.lang.StackOverflowError

1、先贴异常堆栈:2018-07-09 10:20:08.145 WARN 2860 --- [ Thread-2] o.s.c.support.DefaultLifecycleProcessor : Failed to stop bean 'applicationContext' java.lang.StackOverflowError: null at java.lang....

quincyhome的笔记 725

记录排查一次公司OOM的过程

公司后台一段时间老是挂掉,根据日志,发现是OOM的问题.从大的方面来讲,主要采取了两个步骤: 1.让GC日志跑起来,观察到马上进行FULLGC之前,执行命令进行DUMP命令: jmap -dump:format=b,file=./alertdDump 430880..430880 是 alertd的pid..最后会生成一份文件,,该文件的大小就是该后台设置的内存最大的大小. 2.将本分生成的...

qq_26781669的博客 274

Android 错误信息: java.lang.StackOverflowError: stack size 8MB

出现的场景:ViewPager+TabLayout+Fragment 详细的错误信息:java.lang.StackOverflowError: stack size 8MB at android.view.View.invalidateInternal(View.java:13901) at android.

Android 8873

java.lang.StackOverflowError: null at com.fasterxml.jackson.databind.type.TypeFactory._fromAny(Type

遇到一个错误,找了好久 10:04:12.716 [http-nio-8080-exec-10] ERROR o.s.boot.web.support.ErrorPageFilter - Cannot forward to error page for request [/urc/addFunc] as the response has already been committed. As a result, the response may have the wrong status code. If

zhaoyan00123的博客 1251

Bug:java.lang.StackOverflowError: stack size 8MB

在开发的时候遇到了这个Bug:java.lang.StackOverflowError: stack size 8MB Log: 11-27 14:16:37.093 21892-21892/com.xxx.xxx.xxx.debug E/CrashReport: Java Crash Happen cause by main(1) 11-27 14:16:37.183 21892-21892/c...

weixin_34273046的博客 445

一次StackOverFlow问题的排查记录

起因 做为公司的基础架构,要负责给其他的项目组搭建内网的项目,今天在给一个项目搭建内网测试环境的时候,项目无法正常启动,报StackOverflow的问题,问项目负责的开发,他说本地可以正常启动的,但是需要加一个特殊的jvm启动参数 -Xss2m 排查 项目下载下来以后,首先编译就是一个问题,有报了OutOfMemoryError,然后按文档处理配置了内存以后,继续排查。 IDEA编译报错:java.lang.OutOfMemoryError: Java heap space和java.lang.Stac

wejack的专栏 801

栈溢出(StackOverflowError)

背景:系统在测试环境中单服务器,运行了两天。之后,切换成了多服务器(2台),运行了小段时间后,报错: 各种bean 创建失败,最后报错StackOverflowError nested exception is org.springframework.beans.factory.BeanCreationException: Error creating bean with name 'com....

Pianker的博客 1596
上一篇: 特殊表情存数据库处理
下一篇: 远程调用,限制请求超时时间处理
java_xth
博客等级 码龄9年 38粉丝 70原创
评论
成就一亿技术人!
拼手气红包6.0元
还能输入1000个字符
 
 条评论被折叠 查看
添加红包

请填写红包祝福语或标题

个

红包个数最小为10个

元

红包金额最低5元

当前余额3.43元 前往充值 >
需支付:10.00元
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付元
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值