11_Intelligent_Network_Platform

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泛在网(Ambient_Network)

远距离
WOSA
无线网关y
IPSO
IPV6 Hardware and Protocols
IETF 6LowPAN
Phy-Mac Over IPV6
传输网
KNX
IETF ROLL
Routing over Low Power Lossy Networks
IP网络
W-Mbus
有线
ZCL
应用
IEEE
802.15.4
U-Network U-Devices
(Nano Technology)
5C+5Any
Any Time AnyWhere
Health Supply Chain Extension of Services
Disaster
Seamless Interconnection between Persons and Objects Connectivity Reality Intelligence
服务支撑平台
网络架构
网络层
异构网融合下一代承载网
资源和存储管理
云计算
电信网增强
远程控制互联网
标识解析
安全技术 QoS 管理网络管理
M2M无线接入
移动通信网
网络层与感知/延伸层互通
感知 / 延伸层
无线/有线传输
组网技术
信息处理
中间件
数据采集传感器二维条码 RFID 多媒体信息
Mark Weiser
Ubiquitous Network
日本: u-Japan
韩国: IT839
意大利: Ubiquitous
Society
ITU
U-Japan

机器学习与人工智能领域中常用的英语词汇

机器学习与人工智能领域中常用的英语词汇1.General Concepts (基础概念)•Artificial Intelligence (AI) - 人工智能1)Artificial Intelligence (AI) - 人工智能2)Machine Learning (ML) - 机器学习3)Deep Learning (DL) - 深度学习4)Neural Network - 神经网络5)Natural Language Processing (NLP) - 自然语言处理6)Computer Vision - 计算机视觉7)Robotics - 机器人技术8)Speech Recognition - 语音识别9)Expert Systems - 专家系统10)Knowledge Representation - 知识表示11)Pattern Recognition - 模式识别12)Cognitive Computing - 认知计算13)Autonomous Systems - 自主系统14)Human-Machine Interaction - 人机交互15)Intelligent Agents - 智能代理16)Machine Translation - 机器翻译17)Swarm Intelligence - 群体智能18)Genetic Algorithms - 遗传算法19)Fuzzy Logic - 模糊逻辑20)Reinforcement Learning - 强化学习•Machine Learning (ML) - 机器学习1)Machine Learning (ML) - 机器学习2)Artificial Neural Network - 人工神经网络3)Deep Learning - 深度学习4)Supervised Learning - 有监督学习5)Unsupervised Learning - 无监督学习6)Reinforcement Learning - 强化学习7)Semi-Supervised Learning - 半监督学习8)Training Data - 训练数据9)Test Data - 测试数据10)Validation Data - 验证数据11)Feature - 特征12)Label - 标签13)Model - 模型14)Algorithm - 算法15)Regression - 回归16)Classification - 分类17)Clustering - 聚类18)Dimensionality Reduction - 降维19)Overfitting - 过拟合20)Underfitting - 欠拟合•Deep Learning (DL) - 深度学习1)Deep Learning - 深度学习2)Neural Network - 神经网络3)Artificial Neural Network (ANN) - 人工神经网络4)Convolutional Neural Network (CNN) - 卷积神经网络5)Recurrent Neural Network (RNN) - 循环神经网络6)Long Short-Term Memory (LSTM) - 长短期记忆网络7)Gated Recurrent Unit (GRU) - 门控循环单元8)Autoencoder - 自编码器9)Generative Adversarial Network (GAN) - 生成对抗网络10)Transfer Learning - 迁移学习11)Pre-trained Model - 预训练模型12)Fine-tuning - 微调13)Feature Extraction - 特征提取14)Activation Function - 激活函数15)Loss Function - 损失函数16)Gradient Descent - 梯度下降17)Backpropagation - 反向传播18)Epoch - 训练周期19)Batch Size - 批量大小20)Dropout - 丢弃法•Neural Network - 神经网络1)Neural Network - 神经网络2)Artificial Neural Network (ANN) - 人工神经网络3)Deep Neural Network (DNN) - 深度神经网络4)Convolutional Neural Network (CNN) - 卷积神经网络5)Recurrent Neural Network (RNN) - 循环神经网络6)Long Short-Term Memory (LSTM) - 长短期记忆网络7)Gated Recurrent Unit (GRU) - 门控循环单元8)Feedforward Neural Network - 前馈神经网络9)Multi-layer Perceptron (MLP) - 多层感知器10)Radial Basis Function Network (RBFN) - 径向基函数网络11)Hopfield Network - 霍普菲尔德网络12)Boltzmann Machine - 玻尔兹曼机13)Autoencoder - 自编码器14)Spiking Neural Network (SNN) - 脉冲神经网络15)Self-organizing Map (SOM) - 自组织映射16)Restricted Boltzmann Machine (RBM) - 受限玻尔兹曼机17)Hebbian Learning - 海比安学习18)Competitive Learning - 竞争学习19)Neuroevolutionary - 神经进化20)Neuron - 神经元•Algorithm - 算法1)Algorithm - 算法2)Supervised Learning Algorithm - 有监督学习算法3)Unsupervised Learning Algorithm - 无监督学习算法4)Reinforcement Learning Algorithm - 强化学习算法5)Classification Algorithm - 分类算法6)Regression Algorithm - 回归算法7)Clustering Algorithm - 聚类算法8)Dimensionality Reduction Algorithm - 降维算法9)Decision Tree Algorithm - 决策树算法10)Random Forest Algorithm - 随机森林算法11)Support Vector Machine (SVM) Algorithm - 支持向量机算法12)K-Nearest Neighbors (KNN) Algorithm - K近邻算法13)Naive Bayes Algorithm - 朴素贝叶斯算法14)Gradient Descent Algorithm - 梯度下降算法15)Genetic Algorithm - 遗传算法16)Neural Network Algorithm - 神经网络算法17)Deep Learning Algorithm - 深度学习算法18)Ensemble Learning Algorithm - 集成学习算法19)Reinforcement Learning Algorithm - 强化学习算法20)Metaheuristic Algorithm - 元启发式算法•Model - 模型1)Model - 模型2)Machine Learning Model - 机器学习模型3)Artificial Intelligence Model - 人工智能模型4)Predictive Model - 预测模型5)Classification Model - 分类模型6)Regression Model - 回归模型7)Generative Model - 生成模型8)Discriminative Model - 判别模型9)Probabilistic Model - 概率模型10)Statistical Model - 统计模型11)Neural Network Model - 神经网络模型12)Deep Learning Model - 深度学习模型13)Ensemble Model - 集成模型14)Reinforcement Learning Model - 强化学习模型15)Support Vector Machine (SVM) Model - 支持向量机模型16)Decision Tree Model - 决策树模型17)Random Forest Model - 随机森林模型18)Naive Bayes Model - 朴素贝叶斯模型19)Autoencoder Model - 自编码器模型20)Convolutional Neural Network (CNN) Model - 卷积神经网络模型•Dataset - 数据集1)Dataset - 数据集2)Training Dataset - 训练数据集3)Test Dataset - 测试数据集4)Validation Dataset - 验证数据集5)Balanced Dataset - 平衡数据集6)Imbalanced Dataset - 不平衡数据集7)Synthetic Dataset - 合成数据集8)Benchmark Dataset - 基准数据集9)Open Dataset - 开放数据集10)Labeled Dataset - 标记数据集11)Unlabeled Dataset - 未标记数据集12)Semi-Supervised Dataset - 半监督数据集13)Multiclass Dataset - 多分类数据集14)Feature Set - 特征集15)Data Augmentation - 数据增强16)Data Preprocessing - 数据预处理17)Missing Data - 缺失数据18)Outlier Detection - 异常值检测19)Data Imputation - 数据插补20)Metadata - 元数据•Training - 训练1)Training - 训练2)Training Data - 训练数据3)Training Phase - 训练阶段4)Training Set - 训练集5)Training Examples - 训练样本6)Training Instance - 训练实例7)Training Algorithm - 训练算法8)Training Model - 训练模型9)Training Process - 训练过程10)Training Loss - 训练损失11)Training Epoch - 训练周期12)Training Batch - 训练批次13)Online Training - 在线训练14)Offline Training - 离线训练15)Continuous Training - 连续训练16)Transfer Learning - 迁移学习17)Fine-Tuning - 微调18)Curriculum Learning - 课程学习19)Self-Supervised Learning - 自监督学习20)Active Learning - 主动学习•Testing - 测试1)Testing - 测试2)Test Data - 测试数据3)Test Set - 测试集4)Test Examples - 测试样本5)Test Instance - 测试实例6)Test Phase - 测试阶段7)Test Accuracy - 测试准确率8)Test Loss - 测试损失9)Test Error - 测试错误10)Test Metrics - 测试指标11)Test Suite - 测试套件12)Test Case - 测试用例13)Test Coverage - 测试覆盖率14)Cross-Validation - 交叉验证15)Holdout Validation - 留出验证16)K-Fold Cross-Validation - K折交叉验证17)Stratified Cross-Validation - 分层交叉验证18)Test Driven Development (TDD) - 测试驱动开发19)A/B Testing - A/B 测试20)Model Evaluation - 模型评估•Validation - 验证1)Validation - 验证2)Validation Data - 验证数据3)Validation Set - 验证集4)Validation Examples - 验证样本5)Validation Instance - 验证实例6)Validation Phase - 验证阶段7)Validation Accuracy - 验证准确率8)Validation Loss - 验证损失9)Validation Error - 验证错误10)Validation Metrics - 验证指标11)Cross-Validation - 交叉验证12)Holdout Validation - 留出验证13)K-Fold Cross-Validation - K折交叉验证14)Stratified Cross-Validation - 分层交叉验证15)Leave-One-Out Cross-Validation - 留一法交叉验证16)Validation Curve - 验证曲线17)Hyperparameter Validation - 超参数验证18)Model Validation - 模型验证19)Early Stopping - 提前停止20)Validation Strategy - 验证策略•Supervised Learning - 有监督学习1)Supervised Learning - 有监督学习2)Label - 标签3)Feature - 特征4)Target - 目标5)Training Labels - 训练标签6)Training Features - 训练特征7)Training Targets - 训练目标8)Training Examples - 训练样本9)Training Instance - 训练实例10)Regression - 回归11)Classification - 分类12)Predictor - 预测器13)Regression Model - 回归模型14)Classifier - 分类器15)Decision Tree - 决策树16)Support Vector Machine (SVM) - 支持向量机17)Neural Network - 神经网络18)Feature Engineering - 特征工程19)Model Evaluation - 模型评估20)Overfitting - 过拟合21)Underfitting - 欠拟合22)Bias-Variance Tradeoff - 偏差-方差权衡•Unsupervised Learning - 无监督学习1)Unsupervised Learning - 无监督学习2)Clustering - 聚类3)Dimensionality Reduction - 降维4)Anomaly Detection - 异常检测5)Association Rule Learning - 关联规则学习6)Feature Extraction - 特征提取7)Feature Selection - 特征选择8)K-Means - K均值9)Hierarchical Clustering - 层次聚类10)Density-Based Clustering - 基于密度的聚类11)Principal Component Analysis (PCA) - 主成分分析12)Independent Component Analysis (ICA) - 独立成分分析13)T-distributed Stochastic Neighbor Embedding (t-SNE) - t分布随机邻居嵌入14)Gaussian Mixture Model (GMM) - 高斯混合模型15)Self-Organizing Maps (SOM) - 自组织映射16)Autoencoder - 自动编码器17)Latent Variable - 潜变量18)Data Preprocessing - 数据预处理19)Outlier Detection - 异常值检测20)Clustering Algorithm - 聚类算法•Reinforcement Learning - 强化学习1)Reinforcement Learning - 强化学习2)Agent - 代理3)Environment - 环境4)State - 状态5)Action - 动作6)Reward - 奖励7)Policy - 策略8)Value Function - 值函数9)Q-Learning - Q学习10)Deep Q-Network (DQN) - 深度Q网络11)Policy Gradient - 策略梯度12)Actor-Critic - 演员-评论家13)Exploration - 探索14)Exploitation - 开发15)Temporal Difference (TD) - 时间差分16)Markov Decision Process (MDP) - 马尔可夫决策过程17)State-Action-Reward-State-Action (SARSA) - 状态-动作-奖励-状态-动作18)Policy Iteration - 策略迭代19)Value Iteration - 值迭代20)Monte Carlo Methods - 蒙特卡洛方法•Semi-Supervised Learning - 半监督学习1)Semi-Supervised Learning - 半监督学习2)Labeled Data - 有标签数据3)Unlabeled Data - 无标签数据4)Label Propagation - 标签传播5)Self-Training - 自训练6)Co-Training - 协同训练7)Transudative Learning - 传导学习8)Inductive Learning - 归纳学习9)Manifold Regularization - 流形正则化10)Graph-based Methods - 基于图的方法11)Cluster Assumption - 聚类假设12)Low-Density Separation - 低密度分离13)Semi-Supervised Support Vector Machines (S3VM) - 半监督支持向量机14)Expectation-Maximization (EM) - 期望最大化15)Co-EM - 协同期望最大化16)Entropy-Regularized EM - 熵正则化EM17)Mean Teacher - 平均教师18)Virtual Adversarial Training - 虚拟对抗训练19)Tri-training - 三重训练20)Mix Match - 混合匹配•Feature - 特征1)Feature - 特征2)Feature Engineering - 特征工程3)Feature Extraction - 特征提取4)Feature Selection - 特征选择5)Input Features - 输入特征6)Output Features - 输出特征7)Feature Vector - 特征向量8)Feature Space - 特征空间9)Feature Representation - 特征表示10)Feature Transformation - 特征转换11)Feature Importance - 特征重要性12)Feature Scaling - 特征缩放13)Feature Normalization - 特征归一化14)Feature Encoding - 特征编码15)Feature Fusion - 特征融合16)Feature Dimensionality Reduction - 特征维度减少17)Continuous Feature - 连续特征18)Categorical Feature - 分类特征19)Nominal Feature - 名义特征20)Ordinal Feature - 有序特征•Label - 标签1)Label - 标签2)Labeling - 标注3)Ground Truth - 地面真值4)Class Label - 类别标签5)Target Variable - 目标变量6)Labeling Scheme - 标注方案7)Multi-class Labeling - 多类别标注8)Binary Labeling - 二分类标注9)Label Noise - 标签噪声10)Labeling Error - 标注错误11)Label Propagation - 标签传播12)Unlabeled Data - 无标签数据13)Labeled Data - 有标签数据14)Semi-supervised Learning - 半监督学习15)Active Learning - 主动学习16)Weakly Supervised Learning - 弱监督学习17)Noisy Label Learning - 噪声标签学习18)Self-training - 自训练19)Crowdsourcing Labeling - 众包标注20)Label Smoothing - 标签平滑化•Prediction - 预测1)Prediction - 预测2)Forecasting - 预测3)Regression - 回归4)Classification - 分类5)Time Series Prediction - 时间序列预测6)Forecast Accuracy - 预测准确性7)Predictive Modeling - 预测建模8)Predictive Analytics - 预测分析9)Forecasting Method - 预测方法10)Predictive Performance - 预测性能11)Predictive Power - 预测能力12)Prediction Error - 预测误差13)Prediction Interval - 预测区间14)Prediction Model - 预测模型15)Predictive Uncertainty - 预测不确定性16)Forecast Horizon - 预测时间跨度17)Predictive Maintenance - 预测性维护18)Predictive Policing - 预测式警务19)Predictive Healthcare - 预测性医疗20)Predictive Maintenance - 预测性维护•Classification - 分类1)Classification - 分类2)Classifier - 分类器3)Class - 类别4)Classify - 对数据进行分类5)Class Label - 类别标签6)Binary Classification - 二元分类7)Multiclass Classification - 多类分类8)Class Probability - 类别概率9)Decision Boundary - 决策边界10)Decision Tree - 决策树11)Support Vector Machine (SVM) - 支持向量机12)K-Nearest Neighbors (KNN) - K最近邻算法13)Naive Bayes - 朴素贝叶斯14)Logistic Regression - 逻辑回归15)Random Forest - 随机森林16)Neural Network - 神经网络17)SoftMax Function - SoftMax函数18)One-vs-All (One-vs-Rest) - 一对多(一对剩余)19)Ensemble Learning - 集成学习20)Confusion Matrix - 混淆矩阵•Regression - 回归1)Regression Analysis - 回归分析2)Linear Regression - 线性回归3)Multiple Regression - 多元回归4)Polynomial Regression - 多项式回归5)Logistic Regression - 逻辑回归6)Ridge Regression - 岭回归7)Lasso Regression - Lasso回归8)Elastic Net Regression - 弹性网络回归9)Regression Coefficients - 回归系数10)Residuals - 残差11)Ordinary Least Squares (OLS) - 普通最小二乘法12)Ridge Regression Coefficient - 岭回归系数13)Lasso Regression Coefficient - Lasso回归系数14)Elastic Net Regression Coefficient - 弹性网络回归系数15)Regression Line - 回归线16)Prediction Error - 预测误差17)Regression Model - 回归模型18)Nonlinear Regression - 非线性回归19)Generalized Linear Models (GLM) - 广义线性模型20)Coefficient of Determination (R-squared) - 决定系数21)F-test - F检验22)Homoscedasticity - 同方差性23)Heteroscedasticity - 异方差性24)Autocorrelation - 自相关25)Multicollinearity - 多重共线性26)Outliers - 异常值27)Cross-validation - 交叉验证28)Feature Selection - 特征选择29)Feature Engineering - 特征工程30)Regularization - 正则化2.Neural Networks and Deep Learning (神经网络与深度学习)•Convolutional Neural Network (CNN) - 卷积神经网络1)Convolutional Neural Network (CNN) - 卷积神经网络2)Convolution Layer - 卷积层3)Feature Map - 特征图4)Convolution Operation - 卷积操作5)Stride - 步幅6)Padding - 填充7)Pooling Layer - 池化层8)Max Pooling - 最大池化9)Average Pooling - 平均池化10)Fully Connected Layer - 全连接层11)Activation Function - 激活函数12)Rectified Linear Unit (ReLU) - 线性修正单元13)Dropout - 随机失活14)Batch Normalization - 批量归一化15)Transfer Learning - 迁移学习16)Fine-Tuning - 微调17)Image Classification - 图像分类18)Object Detection - 物体检测19)Semantic Segmentation - 语义分割20)Instance Segmentation - 实例分割21)Generative Adversarial Network (GAN) - 生成对抗网络22)Image Generation - 图像生成23)Style Transfer - 风格迁移24)Convolutional Autoencoder - 卷积自编码器25)Recurrent Neural Network (RNN) - 循环神经网络•Recurrent Neural Network (RNN) - 循环神经网络1)Recurrent Neural Network (RNN) - 循环神经网络2)Long Short-Term Memory (LSTM) - 长短期记忆网络3)Gated Recurrent Unit (GRU) - 门控循环单元4)Sequence Modeling - 序列建模5)Time Series Prediction - 时间序列预测6)Natural Language Processing (NLP) - 自然语言处理7)Text Generation - 文本生成8)Sentiment Analysis - 情感分析9)Named Entity Recognition (NER) - 命名实体识别10)Part-of-Speech Tagging (POS Tagging) - 词性标注11)Sequence-to-Sequence (Seq2Seq) - 序列到序列12)Attention Mechanism - 注意力机制13)Encoder-Decoder Architecture - 编码器-解码器架构14)Bidirectional RNN - 双向循环神经网络15)Teacher Forcing - 强制教师法16)Backpropagation Through Time (BPTT) - 通过时间的反向传播17)Vanishing Gradient Problem - 梯度消失问题18)Exploding Gradient Problem - 梯度爆炸问题19)Language Modeling - 语言建模20)Speech Recognition - 语音识别•Long Short-Term Memory (LSTM) - 长短期记忆网络1)Long Short-Term Memory (LSTM) - 长短期记忆网络2)Cell State - 细胞状态3)Hidden State - 隐藏状态4)Forget Gate - 遗忘门5)Input Gate - 输入门6)Output Gate - 输出门7)Peephole Connections - 窥视孔连接8)Gated Recurrent Unit (GRU) - 门控循环单元9)Vanishing Gradient Problem - 梯度消失问题10)Exploding Gradient Problem - 梯度爆炸问题11)Sequence Modeling - 序列建模12)Time Series Prediction - 时间序列预测13)Natural Language Processing (NLP) - 自然语言处理14)Text Generation - 文本生成15)Sentiment Analysis - 情感分析16)Named Entity Recognition (NER) - 命名实体识别17)Part-of-Speech Tagging (POS Tagging) - 词性标注18)Attention Mechanism - 注意力机制19)Encoder-Decoder Architecture - 编码器-解码器架构20)Bidirectional LSTM - 双向长短期记忆网络•Attention Mechanism - 注意力机制1)Attention Mechanism - 注意力机制2)Self-Attention - 自注意力3)Multi-Head Attention - 多头注意力4)Transformer - 变换器5)Query - 查询6)Key - 键7)Value - 值8)Query-Value Attention - 查询-值注意力9)Dot-Product Attention - 点积注意力10)Scaled Dot-Product Attention - 缩放点积注意力11)Additive Attention - 加性注意力12)Context Vector - 上下文向量13)Attention Score - 注意力分数14)SoftMax Function - SoftMax函数15)Attention Weight - 注意力权重16)Global Attention - 全局注意力17)Local Attention - 局部注意力18)Positional Encoding - 位置编码19)Encoder-Decoder Attention - 编码器-解码器注意力20)Cross-Modal Attention - 跨模态注意力•Generative Adversarial Network (GAN) - 生成对抗网络1)Generative Adversarial Network (GAN) - 生成对抗网络2)Generator - 生成器3)Discriminator - 判别器4)Adversarial Training - 对抗训练5)Minimax Game - 极小极大博弈6)Nash Equilibrium - 纳什均衡7)Mode Collapse - 模式崩溃8)Training Stability - 训练稳定性9)Loss Function - 损失函数10)Discriminative Loss - 判别损失11)Generative Loss - 生成损失12)Wasserstein GAN (WGAN) - Wasserstein GAN（WGAN）13)Deep Convolutional GAN (DCGAN) - 深度卷积生成对抗网络（DCGAN）14)Conditional GAN (c GAN) - 条件生成对抗网络（c GAN）15)Style GAN - 风格生成对抗网络16)Cycle GAN - 循环生成对抗网络17)Progressive Growing GAN (PGGAN) - 渐进式增长生成对抗网络（PGGAN）18)Self-Attention GAN (SAGAN) - 自注意力生成对抗网络（SAGAN）19)Big GAN - 大规模生成对抗网络20)Adversarial Examples - 对抗样本•Encoder-Decoder - 编码器-解码器1)Encoder-Decoder Architecture - 编码器-解码器架构2)Encoder - 编码器3)Decoder - 解码器4)Sequence-to-Sequence Model (Seq2Seq) - 序列到序列模型5)State Vector - 状态向量6)Context Vector - 上下文向量7)Hidden State - 隐藏状态8)Attention Mechanism - 注意力机制9)Teacher Forcing - 强制教师法10)Beam Search - 束搜索11)Recurrent Neural Network (RNN) - 循环神经网络12)Long Short-Term Memory (LSTM) - 长短期记忆网络13)Gated Recurrent Unit (GRU) - 门控循环单元14)Bidirectional Encoder - 双向编码器15)Greedy Decoding - 贪婪解码16)Masking - 遮盖17)Dropout - 随机失活18)Embedding Layer - 嵌入层19)Cross-Entropy Loss - 交叉熵损失20)Tokenization - 令牌化•Transfer Learning - 迁移学习1)Transfer Learning - 迁移学习2)Source Domain - 源领域3)Target Domain - 目标领域4)Fine-Tuning - 微调5)Domain Adaptation - 领域自适应6)Pre-Trained Model - 预训练模型7)Feature Extraction - 特征提取8)Knowledge Transfer - 知识迁移9)Unsupervised Domain Adaptation - 无监督领域自适应10)Semi-Supervised Domain Adaptation - 半监督领域自适应11)Multi-Task Learning - 多任务学习12)Data Augmentation - 数据增强13)Task Transfer - 任务迁移14)Model Agnostic Meta-Learning (MAML) - 与模型无关的元学习（MAML）15)One-Shot Learning - 单样本学习16)Zero-Shot Learning - 零样本学习17)Few-Shot Learning - 少样本学习18)Knowledge Distillation - 知识蒸馏19)Representation Learning - 表征学习20)Adversarial Transfer Learning - 对抗迁移学习•Pre-trained Models - 预训练模型1)Pre-trained Model - 预训练模型2)Transfer Learning - 迁移学习3)Fine-Tuning - 微调4)Knowledge Transfer - 知识迁移5)Domain Adaptation - 领域自适应6)Feature Extraction - 特征提取7)Representation Learning - 表征学习8)Language Model - 语言模型9)Bidirectional Encoder Representations from Transformers (BERT) - 双向编码器结构转换器10)Generative Pre-trained Transformer (GPT) - 生成式预训练转换器11)Transformer-based Models - 基于转换器的模型12)Masked Language Model (MLM) - 掩蔽语言模型13)Cloze Task - 填空任务14)Tokenization - 令牌化15)Word Embeddings - 词嵌入16)Sentence Embeddings - 句子嵌入17)Contextual Embeddings - 上下文嵌入18)Self-Supervised Learning - 自监督学习19)Large-Scale Pre-trained Models - 大规模预训练模型•Loss Function - 损失函数1)Loss Function - 损失函数2)Mean Squared Error (MSE) - 均方误差3)Mean Absolute Error (MAE) - 平均绝对误差4)Cross-Entropy Loss - 交叉熵损失5)Binary Cross-Entropy Loss - 二元交叉熵损失6)Categorical Cross-Entropy Loss - 分类交叉熵损失7)Hinge Loss - 合页损失8)Huber Loss - Huber损失9)Wasserstein Distance - Wasserstein距离10)Triplet Loss - 三元组损失11)Contrastive Loss - 对比损失12)Dice Loss - Dice损失13)Focal Loss - 焦点损失14)GAN Loss - GAN损失15)Adversarial Loss - 对抗损失16)L1 Loss - L1损失17)L2 Loss - L2损失18)Huber Loss - Huber损失19)Quantile Loss - 分位数损失•Activation Function - 激活函数1)Activation Function - 激活函数2)Sigmoid Function - Sigmoid函数3)Hyperbolic Tangent Function (Tanh) - 双曲正切函数4)Rectified Linear Unit (Re LU) - 矩形线性单元5)Parametric Re LU (P Re LU) - 参数化Re LU6)Exponential Linear Unit (ELU) - 指数线性单元7)Swish Function - Swish函数8)Softplus Function - Soft plus函数9)Softmax Function - SoftMax函数10)Hard Tanh Function - 硬双曲正切函数11)Softsign Function - Softsign函数12)GELU (Gaussian Error Linear Unit) - GELU（高斯误差线性单元）13)Mish Function - Mish函数14)CELU (Continuous Exponential Linear Unit) - CELU（连续指数线性单元）15)Bent Identity Function - 弯曲恒等函数16)Gaussian Error Linear Units (GELUs) - 高斯误差线性单元17)Adaptive Piecewise Linear (APL) - 自适应分段线性函数18)Radial Basis Function (RBF) - 径向基函数•Backpropagation - 反向传播1)Backpropagation - 反向传播2)Gradient Descent - 梯度下降3)Partial Derivative - 偏导数4)Chain Rule - 链式法则5)Forward Pass - 前向传播6)Backward Pass - 反向传播7)Computational Graph - 计算图8)Neural Network - 神经网络9)Loss Function - 损失函数10)Gradient Calculation - 梯度计算11)Weight Update - 权重更新12)Activation Function - 激活函数13)Optimizer - 优化器14)Learning Rate - 学习率15)Mini-Batch Gradient Descent - 小批量梯度下降16)Stochastic Gradient Descent (SGD) - 随机梯度下降17)Batch Gradient Descent - 批量梯度下降18)Momentum - 动量19)Adam Optimizer - Adam优化器20)Learning Rate Decay - 学习率衰减•Gradient Descent - 梯度下降1)Gradient Descent - 梯度下降2)Stochastic Gradient Descent (SGD) - 随机梯度下降3)Mini-Batch Gradient Descent - 小批量梯度下降4)Batch Gradient Descent - 批量梯度下降5)Learning Rate - 学习率6)Momentum - 动量7)Adaptive Moment Estimation (Adam) - 自适应矩估计8)RMSprop - 均方根传播9)Learning Rate Schedule - 学习率调度10)Convergence - 收敛11)Divergence - 发散12)Adagrad - 自适应学习速率方法13)Adadelta - 自适应增量学习率方法14)Adamax - 自适应矩估计的扩展版本15)Nadam - Nesterov Accelerated Adaptive Moment Estimation16)Learning Rate Decay - 学习率衰减17)Step Size - 步长18)Conjugate Gradient Descent - 共轭梯度下降19)Line Search - 线搜索20)Newton's Method - 牛顿法•Learning Rate - 学习率1)Learning Rate - 学习率2)Adaptive Learning Rate - 自适应学习率3)Learning Rate Decay - 学习率衰减4)Initial Learning Rate - 初始学习率5)Step Size - 步长6)Momentum - 动量7)Exponential Decay - 指数衰减8)Annealing - 退火9)Cyclical Learning Rate - 循环学习率10)Learning Rate Schedule - 学习率调度11)Warm-up - 预热12)Learning Rate Policy - 学习率策略13)Learning Rate Annealing - 学习率退火14)Cosine Annealing - 余弦退火15)Gradient Clipping - 梯度裁剪16)Adapting Learning Rate - 适应学习率17)Learning Rate Multiplier - 学习率倍增器18)Learning Rate Reduction - 学习率降低19)Learning Rate Update - 学习率更新20)Scheduled Learning Rate - 定期学习率•Batch Size - 批量大小1)Batch Size - 批量大小2)Mini-Batch - 小批量3)Batch Gradient Descent - 批量梯度下降4)Stochastic Gradient Descent (SGD) - 随机梯度下降5)Mini-Batch Gradient Descent - 小批量梯度下降6)Online Learning - 在线学习7)Full-Batch - 全批量8)Data Batch - 数据批次9)Training Batch - 训练批次10)Batch Normalization - 批量归一化11)Batch-wise Optimization - 批量优化12)Batch Processing - 批量处理13)Batch Sampling - 批量采样14)Adaptive Batch Size - 自适应批量大小15)Batch Splitting - 批量分割16)Dynamic Batch Size - 动态批量大小17)Fixed Batch Size - 固定批量大小18)Batch-wise Inference - 批量推理19)Batch-wise Training - 批量训练20)Batch Shuffling - 批量洗牌•Epoch - 训练周期1)Training Epoch - 训练周期2)Epoch Size - 周期大小3)Early Stopping - 提前停止4)Validation Set - 验证集5)Training Set - 训练集6)Test Set - 测试集7)Overfitting - 过拟合8)Underfitting - 欠拟合9)Model Evaluation - 模型评估10)Model Selection - 模型选择11)Hyperparameter Tuning - 超参数调优12)Cross-Validation - 交叉验证13)K-fold Cross-Validation - K折交叉验证14)Stratified Cross-Validation - 分层交叉验证15)Leave-One-Out Cross-Validation (LOOCV) - 留一法交叉验证16)Grid Search - 网格搜索17)Random Search - 随机搜索18)Model Complexity - 模型复杂度19)Learning Curve - 学习曲线20)Convergence - 收敛3.Machine Learning Techniques and Algorithms (机器学习技术与算法)•Decision Tree - 决策树1)Decision Tree - 决策树2)Node - 节点3)Root Node - 根节点4)Leaf Node - 叶节点5)Internal Node - 内部节点6)Splitting Criterion - 分裂准则7)Gini Impurity - 基尼不纯度8)Entropy - 熵9)Information Gain - 信息增益10)Gain Ratio - 增益率11)Pruning - 剪枝12)Recursive Partitioning - 递归分割13)CART (Classification and Regression Trees) - 分类回归树14)ID3 (Iterative Dichotomiser 3) - 迭代二叉树315)C4.5 (successor of ID3) - C4.5（ID3的后继者）16)C5.0 (successor of C4.5) - C5.0（C4.5的后继者）17)Split Point - 分裂点18)Decision Boundary - 决策边界19)Pruned Tree - 剪枝后的树20)Decision Tree Ensemble - 决策树集成•Random Forest - 随机森林1)Random Forest - 随机森林2)Ensemble Learning - 集成学习3)Bootstrap Sampling - 自助采样4)Bagging (Bootstrap Aggregating) - 装袋法5)Out-of-Bag (OOB) Error - 袋外误差6)Feature Subset - 特征子集7)Decision Tree - 决策树8)Base Estimator - 基础估计器9)Tree Depth - 树深度10)Randomization - 随机化11)Majority Voting - 多数投票12)Feature Importance - 特征重要性13)OOB Score - 袋外得分14)Forest Size - 森林大小15)Max Features - 最大特征数16)Min Samples Split - 最小分裂样本数17)Min Samples Leaf - 最小叶节点样本数18)Gini Impurity - 基尼不纯度19)Entropy - 熵20)Variable Importance - 变量重要性•Support Vector Machine (SVM) - 支持向量机1)Support Vector Machine (SVM) - 支持向量机2)Hyperplane - 超平面3)Kernel Trick - 核技巧4)Kernel Function - 核函数5)Margin - 间隔6)Support Vectors - 支持向量7)Decision Boundary - 决策边界8)Maximum Margin Classifier - 最大间隔分类器9)Soft Margin Classifier - 软间隔分类器10) C Parameter - C参数11)Radial Basis Function (RBF) Kernel - 径向基函数核12)Polynomial Kernel - 多项式核13)Linear Kernel - 线性核14)Quadratic Kernel - 二次核15)Gaussian Kernel - 高斯核16)Regularization - 正则化17)Dual Problem - 对偶问题18)Primal Problem - 原始问题19)Kernelized SVM - 核化支持向量机20)Multiclass SVM - 多类支持向量机•K-Nearest Neighbors (KNN) - K-最近邻1)K-Nearest Neighbors (KNN) - K-最近邻2)Nearest Neighbor - 最近邻3)Distance Metric - 距离度量4)Euclidean Distance - 欧氏距离5)Manhattan Distance - 曼哈顿距离6)Minkowski Distance - 闵可夫斯基距离7)Cosine Similarity - 余弦相似度8)K Value - K值9)Majority Voting - 多数投票10)Weighted KNN - 加权KNN11)Radius Neighbors - 半径邻居12)Ball Tree - 球树13)KD Tree - KD树14)Locality-Sensitive Hashing (LSH) - 局部敏感哈希15)Curse of Dimensionality - 维度灾难16)Class Label - 类标签17)Training Set - 训练集18)Test Set - 测试集19)Validation Set - 验证集20)Cross-Validation - 交叉验证•Naive Bayes - 朴素贝叶斯1)Naive Bayes - 朴素贝叶斯2)Bayes' Theorem - 贝叶斯定理3)Prior Probability - 先验概率4)Posterior Probability - 后验概率5)Likelihood - 似然6)Class Conditional Probability - 类条件概率7)Feature Independence Assumption - 特征独立假设8)Multinomial Naive Bayes - 多项式朴素贝叶斯9)Gaussian Naive Bayes - 高斯朴素贝叶斯10)Bernoulli Naive Bayes - 伯努利朴素贝叶斯11)Laplace Smoothing - 拉普拉斯平滑12)Add-One Smoothing - 加一平滑13)Maximum A Posteriori (MAP) - 最大后验概率14)Maximum Likelihood Estimation (MLE) - 最大似然估计15)Classification - 分类16)Feature Vectors - 特征向量17)Training Set - 训练集18)Test Set - 测试集19)Class Label - 类标签20)Confusion Matrix - 混淆矩阵•Clustering - 聚类1)Clustering - 聚类2)Centroid - 质心3)Cluster Analysis - 聚类分析4)Partitioning Clustering - 划分式聚类5)Hierarchical Clustering - 层次聚类6)Density-Based Clustering - 基于密度的聚类7)K-Means Clustering - K均值聚类8)K-Medoids Clustering - K中心点聚类9)DBSCAN (Density-Based Spatial Clustering of Applications with Noise) - 基于密度的空间聚类算法10)Agglomerative Clustering - 聚合式聚类11)Dendrogram - 系统树图12)Silhouette Score - 轮廓系数13)Elbow Method - 肘部法则14)Clustering Validation - 聚类验证15)Intra-cluster Distance - 类内距离16)Inter-cluster Distance - 类间距离17)Cluster Cohesion - 类内连贯性18)Cluster Separation - 类间分离度19)Cluster Assignment - 聚类分配20)Cluster Label - 聚类标签•K-Means - K-均值1)K-Means - K-均值2)Centroid - 质心3)Cluster - 聚类4)Cluster Center - 聚类中心5)Cluster Assignment - 聚类分配6)Cluster Analysis - 聚类分析7)K Value - K值8)Elbow Method - 肘部法则9)Inertia - 惯性10)Silhouette Score - 轮廓系数11)Convergence - 收敛12)Initialization - 初始化13)Euclidean Distance - 欧氏距离14)Manhattan Distance - 曼哈顿距离15)Distance Metric - 距离度量16)Cluster Radius - 聚类半径17)Within-Cluster Variation - 类内变异18)Cluster Quality - 聚类质量19)Clustering Algorithm - 聚类算法20)Clustering Validation - 聚类验证•Dimensionality Reduction - 降维1)Dimensionality Reduction - 降维2)Feature Extraction - 特征提取3)Feature Selection - 特征选择4)Principal Component Analysis (PCA) - 主成分分析5)Singular Value Decomposition (SVD) - 奇异值分解6)Linear Discriminant Analysis (LDA) - 线性判别分析7)t-Distributed Stochastic Neighbor Embedding (t-SNE) - t-分布随机邻域嵌入8)Autoencoder - 自编码器9)Manifold Learning - 流形学习10)Locally Linear Embedding (LLE) - 局部线性嵌入11)Isomap - 等度量映射12)Uniform Manifold Approximation and Projection (UMAP) - 均匀流形逼近与投影13)Kernel PCA - 核主成分分析14)Non-negative Matrix Factorization (NMF) - 非负矩阵分解15)Independent Component Analysis (ICA) - 独立成分分析16)Variational Autoencoder (VAE) - 变分自编码器17)Sparse Coding - 稀疏编码18)Random Projection - 随机投影19)Neighborhood Preserving Embedding (NPE) - 保持邻域结构的嵌入20)Curvilinear Component Analysis (CCA) - 曲线成分分析•Principal Component Analysis (PCA) - 主成分分析1)Principal Component Analysis (PCA) - 主成分分析2)Eigenvector - 特征向量3)Eigenvalue - 特征值4)Covariance Matrix - 协方差矩阵。

verizon-Core-network-transformation

UNI Provisioning Web Portal
• 40G per λ • 100 λs • Mesh • >1000km reach • Ethernet Capable • Packet Enabled • ADM on a λ • Uni-directional Optics • Optical Broadcast
1 0 0
Current Average BW Growth Rate
100 Mbps
•NA FTTN sustained BW in access •NTT ADSL Advertised BW •NTT Fiber Advertised BW (peak)
Verizon FiOS BW 40 Mbps
Overlay over Common Backbone
E1 E2 E3 E4 A1 A2 B3 B4 B4 B1 B2 B L2 Virtual Circuits B B3 E3 E4 B1 B2 A1 A2 E1 E2
Software Logical Routers
E1 E2 E3 E4 A1 A2 B3 B4
24 Mbps
NA FTTN sustained BW
10 Mbps
1 0
12 Mbps
8 Mbps 1.5 Mbps
1
0.5 Mbps
0 1 .
0 0 1 .
Infonetics
0
Global BW growth trend accelerates to close to 10x per 3-4 years. Introduction of video causes a step increase . Will this continue?

软件工程英文参考文献(优秀范文105个)

当前,计算机技术与网络技术得到了较快发展,计算机软件工程进入到社会各个领域当中,使很多操作实现了自动化,得到了人们的普遍欢迎,解放了大量的人力.为了适应时代的发展,社会各个领域大力引进计算机软件工程.下面是软件工程英文参考文献105个，供大家参考阅读。

软件工程英文参考文献一：[1]Carine Khalil,Sabine Khalil. Exploring knowledge management in agile software development organizations[J]. International Entrepreneurship and Management Journal,2020,16(4).[2]Kevin A. Gary,Ruben Acuna,Alexandra Mehlhase,Robert Heinrichs,Sohum Sohoni. SCALING TO MEET THE ONLINE DEMAND IN SOFTWARE ENGINEERING[J]. International Journal on Innovations in Online Education,2020,4(1).[3]Hosseini Hadi,Zirakjou Abbas,Goodarzi Vahabodin,Mousavi Seyyed Mohammad,Khonakdar Hossein Ali,Zamanlui Soheila. Lightweight aerogels based on bacterial cellulose/silver nanoparticles/polyaniline with tuning morphology of polyaniline and application in soft tissue engineering.[J]. International journal of biological macromolecules,2020,152.[4]Dylan G. Kelly,Patrick Seeling. Introducing underrepresented high school students to software engineering: Using the micro:bit microcontroller to program connected autonomous cars[J]. Computer Applications in Engineering Education,2020,28(3).[5]. Soft Computing; Research Conducted at School of Computing Science and Engineering Has Updated Our Knowledge about Soft Computing (Indeterminate Likert scale: feedback based on neutrosophy, its distance measures and clustering algorithm)[J]. News of Science,2020.[6]. Engineering; New Engineering Findings from Hanyang University Outlined (Can-based Aging Monitoring Technique for Automotive Asics With Efficient Soft Error Resilience)[J]. Journal of Transportation,2020.[7]. Engineering - Software Engineering; New Findings from University of Michigan in the Area of Software Engineering Reported (Multi-criteria Test Cases Selection for Model Transformations)[J]. Journal of Transportation,2020.[8]Tamas Galli,Francisco Chiclana,Francois Siewe. Software Product Quality Models, Developments, Trends, and Evaluation[J]. SN Computer Science,2020,1(2).[9]. Infotech; Infotech Joins BIM for Bridges and Structures Transportation Pooled Fund Project as an Official Software Advisor[J]. Computer TechnologyJournal,2020.[10]. Engineering; Study Findings from Beijing Jiaotong University Provide New Insights into Engineering (Analyzing Software Rejuvenation Techniques In a Virtualized System: Service Provider and User Views)[J]. Computer Technology Journal,2020.[11]. Soft Computing; Data on Soft Computing Reported by Researchers at Sakarya University (An exponential jerk system, its fractional-order form with dynamical analysis and engineering application)[J]. Computer Technology Journal,2020.[12]. Engineering; Studies from Henan University Yield New Data on Engineering (Extracting Phrases As Software Features From Overlapping Sentence Clusters In Product Descriptions)[J]. Computer Technology Journal,2020.[13]. Engineering; Data from Nanjing University of Aeronautics and Astronautics Provide New Insights into Engineering (A Systematic Study to Improve the Requirements Engineering Process in the Domain of Global Software Development)[J]. Computer Technology Journal,2020.[14]. Soft Computing; Investigators at Air Force Engineering University Report Findings in Soft Computing (Evidential model for intuitionistic fuzzy multi-attribute group decision making)[J]. Computer Technology Journal,2020.[15]. Engineering; Researchers from COMSATS University Islamabad Describe Findings in Engineering (A Deep CNN Ensemble Framework for Efficient DDoS Attack Detection in Software Defined Networks)[J]. Computer Technology Journal,2020.[16]Pedro Delgado-Pérez,Francisco Chicano. An Experimental and Practical Study on the Equivalent Mutant Connection: An Evolutionary Approach[J]. Information and Software Technology,2020.[17]Koehler Leman Julia,Weitzner Brian D,Renfrew P Douglas,Lewis Steven M,Moretti Rocco,Watkins Andrew M,Mulligan Vikram Khipple,Lyskov Sergey,Adolf-Bryfogle Jared,Labonte Jason W,Krys Justyna,Bystroff Christopher,Schief William,Gront Dominik,Schueler-Furman Ora,Baker David,Bradley Philip,Dunbrack Roland,Kortemme Tanja,Leaver-Fay Andrew,Strauss Charlie E M,Meiler Jens,Kuhlman Brian,Gray Jeffrey J,Bonneau Richard. Better together: Elements of successful scientific software development in a distributed collaborative community.[J]. PLoS computational biology,2020,16(5).[18]. Mathematics; Data on Mathematics Reported by Researchers at Thapar Institute of Engineering and Technology (Algorithms Based on COPRAS and Aggregation Operators with New Information Measures for Possibility Intuitionistic Fuzzy SoftDecision-Making)[J]. Journal of Mathematics,2020.[19]. Engineering - Medical and Biological Engineering; Reports from Heriot-Watt University Describe Recent Advances in Medical and Biological Engineering (A Novel Palpation-based Method for Tumor Nodule Quantification In Soft Tissue-computational Framework and Experimental Validation)[J]. Journal of Engineering,2020.[20]. Engineering - Industrial Engineering; Studies from Xi'an Jiaotong University Have Provided New Data on Industrial Engineering (Dc Voltage Control Strategy of Three-terminal Medium-voltage Power Electronic Transformer-based Soft Normally Open Points)[J]. Journal of Engineering,2020.[21]. Engineering; Reports from Hohai University Add New Data to Findings in Engineering (Soft Error Resilience of Deep Residual Networks for Object Recognition)[J]. Journal of Engineering,2020.[22]. Engineering - Mechanical Engineering; Study Data from K.N. Toosi University of Technology Update Understanding of Mechanical Engineering (Coupled Directional Dilation-Damage Approach to Model the Cyclic-Undrained Response of Soft Clay under Pure Principal Stress Axes Rotation)[J]. Journal of Engineering,2020.[23]. Soft Computing; Researchers from Abes Engineering College Report Details of New Studies and Findings in the Area of Soft Computing (An intelligent personalized web blog searching technique using fuzzy-based feedback recurrent neural network)[J]. Network Weekly News,2020.[24]. Engineering; Studies from University of Alexandria in the Area of Engineering Reported (Software Defined Network-Based Management for Enhanced 5G Network Services)[J]. Network Weekly News,2020.[25]. Soft Computing; Data on Soft Computing Discussed by Researchers at Department of Electrical and Communication Engineering [A metaheuristic optimization model for spectral allocation in cognitive networks based on ant colony algorithm (M-ACO)][J]. Computer Technology Journal,2020.[26]. Engineering - Software Engineering; Complutense University Madrid Reports Findings in Software Engineering (Recolibry Suite: a Set of Intelligent Tools for the Development of Recommender Systems)[J]. Computer Technology Journal,2020.[27]. Engineering - Software Engineering; Data on Software Engineering Reported by Researchers at Gautam Buddha University (A novel quality prediction model for component based software system using ACO-NM optimized extreme learning machine)[J]. Computer Technology Journal,2020.[28]. Soft Computing; New Soft Computing Study Findings Recently Were Reported by Researchers at University College of Engineering (A novel QIM-DCT based fusion approach for classification of remote sensing images via PSO and SVM models)[J]. Computer Technology Journal,2020.[29]Morshedloo Fatemeh,Khoshfetrat Ali Baradar,Kazemi Davoud,Ahmadian Mehri. Gelatin improves peroxidase-mediated alginate hydrogel characteristics as a potential injectable hydrogel for soft tissue engineering applications.[J]. Journal of biomedical materials research. Part B, Applied biomaterials,2020.[30]Jung-Chieh Lee,Chung-Yang Chen. Exploring the team dynamic learning process in software process tailoring performance[J]. Journal of Enterprise Information Management,2020,33(3).[31]. Soft Computing; Study Results from Velammal Engineering College in the Area of Soft Computing Reported (Efficient routing in UASN during the thermohaline environment condition to improve the propagation delay and throughput)[J]. Mathematics Week,2020.[32]. Soft Matter; Findings from School of Materials Science and Engineering Provide New Insights into Soft Matter (A practical guide to active colloids: choosing synthetic model systems for soft matter physics research)[J]. Physics Week,2020.[33]Julio César Puche-Regaliza,Alfredo Jiménez,Pablo Arranz-Val. Diagnosis of Software Projects Based on the Viable System Model[J]. Systemic Practice and Action Research,2020,33(1).[34]Meinert Edward,Milne-Ives Madison,Surodina Svitlana,Lam Ching. Agile requirements engineering and software planning for a digital health platform to engage the effects of isolation caused by social distancing: A case study and feasibility study protocol.[J]. JMIR public health and surveillance,2020.[35]. Engineering - Civil Engineering; Studies Conducted at Shandong Jianzhu University on Civil Engineering Recently Published (Seismic Response Analysis and Control of Frame Structures with Soft First Storey under Near-Fault Ground Motions)[J]. Journal of Engineering,2020.软件工程英文参考文献二：[36]Chao-ze Lu,Guo-sun Zeng,Ying-jie Xie. Bigraph specification of software architecture and evolution analysis in mobile computing environment[J]. Future Generation Computer Systems,2020,108.[37]Ompal Singh, Saurabh Panwar, P. K. Kapur.. Determining SoftwareTime-to-Market and Testing Stop Time when Release Time is a Change-Point[J]. International Journal of Mathematical, Engineering and Management Sciences,2020,5(2).[38]Ayushi Verma,Neetu Sardana,Sangeeta Lal. Developer Recommendation for Stack Exchange Software Engineering Q&A Website based on K-Means clustering and Developer Social Network Metric[J]. Procedia Computer Science,2020,167.[39]Jagdeep Singh,Sachin Bagga,Ranjodh Kaur. Software-based Prediction of Liver Disease with Feature Selection and Classification Techniques[J]. Procedia Computer Science,2020,167.[40]. Engineering - Software Engineering; Studies from Concordia University Update Current Data on Software Engineering (On the impact of using trivial packages: an empirical case study on npm and PyPI)[J]. Computer Technology Journal,2020.[41]. Engineering - Software Engineering; Study Findings from University of Alberta Broaden Understanding of Software Engineering (Building the perfect game - an empirical study of game modifications)[J]. Computer Technology Journal,2020.[42]. Engineering - Software Engineering; Investigators at National Research Council (CNR) Detail Findings in Software Engineering [A Framework for Quantitative Modeling and Analysis of Highly (Re)Configurable Systems][J]. Computer Technology Journal,2020.[43]. Engineering - Knowledge Engineering; Data from University of Paris Saclay Provide New Insights into Knowledge Engineering (Dynamic monitoring of software use with recurrent neural networks)[J]. Computer Technology Journal,2020.[44]. Engineering - Circuits Research; Findings from Federal University Santa Maria Yields New Data on Circuits Research (A New Cpfsk Demodulation Approach for Software Defined Radio)[J]. Computer Technology Journal,2020.[45]. Soft Computing; Investigators from Lovely Professional University Release New Data on Soft Computing (An intensify Harris Hawks optimizer for numerical and engineering optimization problems)[J]. Computer Technology Journal,2020.[46]. GlobalLogic Inc.; GlobalLogic Acquires Meelogic Consulting AG, a European Healthcare and Automotive-Focused Software Engineering Services Firm[J]. Computer Technology Journal,2020.[47]. Engineering - Circuits and Systems Research; Data on Circuits and Systems Research Described by Researchers at Northeastern University (Softcharge: Software Defined Multi-device Wireless Charging Over Large Surfaces)[J]. TelecommunicationsWeekly,2020.[48]. Soft Computing; Researchers from Department of Electrical and Communication Engineering Report on Findings in Soft Computing (Dynamic Histogram Equalization for contrast enhancement for digital images)[J]. Technology News Focus,2020.[49]Mohamed Ellithey Barghoth,Akram Salah,Manal A. Ismail. A Comprehensive Software Project Management Framework[J]. Journal of Computer and Communications,2020,08(03).[50]. Soft Computing; Researchers from Air Force Engineering University Describe Findings in Soft Computing (Random orthocenter strategy in interior search algorithm and its engineering application)[J]. Journal of Mathematics,2020.[51]. Soft Computing; Study Findings on Soft Computing Are Outlined in Reports from Department of Mechanical Engineering (Constrained design optimization of selected mechanical system components using Rao algorithms)[J]. Mathematics Week,2020.[52]Iqbal Javed,Ahmad Rodina B,Khan Muzafar,Fazal-E-Amin,Alyahya Sultan,Nizam Nasir Mohd Hairul,Akhunzada Adnan,Shoaib Muhammad. Requirements engineering issues causing software development outsourcing failure.[J]. PloS one,2020,15(4).[53]Raymond C.Z. Cohen,Simon M. Harrison,Paul W. Cleary. Dive Mechanic: Bringing 3D virtual experimentation using biomechanical modelling to elite level diving with the Workspace workflow engine[J]. Mathematics and Computers in Simulation,2020,175.[54]Emelie Engstr?m,Margaret-Anne Storey,Per Runeson,Martin H?st,Maria Teresa Baldassarre. How software engineering research aligns with design science: a review[J]. Empirical Software Engineering,2020(prepublish).[55]Christian Lettner,Michael Moser,Josef Pichler. An integrated approach for power transformer modeling and manufacturing[J]. Procedia Manufacturing,2020,42.[56]. Engineering - Mechanical Engineering; New Findings from Leibniz University Hannover Update Understanding of Mechanical Engineering (A finite element for soft tissue deformation based on the absolute nodal coordinate formulation)[J]. Computer Technology Journal,2020.[57]. Science - Social Science; Studies from University of Burgos Yield New Information about Social Science (Diagnosis of Software Projects Based on the Viable System Model)[J]. Computer Technology Journal,2020.[58]. Technology - Powder Technology; Investigators at Research Center Pharmaceutical Engineering GmbH Discuss Findings in Powder Technology [Extended Validation and Verification of Xps/avl-fire (Tm), a Computational Cfd-dem Software Platform][J]. Computer Technology Journal,2020.[59]Guadalupe-Isaura Trujillo-Tzanahua,Ulises Juárez-Martínez,Alberto-Alfonso Aguilar-Lasserre,María-Karen Cortés-Verdín,Catherine Azzaro-Pantel. Multiple software product lines to configure applications of internet of things[J]. IET Software,2020,14(2).[60]Eduardo Juárez,Rocio Aldeco-Pérez,Jose.Manuel Velázquez. Academic approach to transform organisations: one engineer at a time[J]. IET Software,2020,14(2).[61]Dennys García-López,Marco Segura-Morales,Edson Loza-Aguirre. Improving the quality and quantity of functional and non-functional requirements obtained during requirements elicitation stage for the development of e-commerce mobile applications: an alternative reference process model[J]. IET Software,2020,14(2).[62]. Guest Editorial: Software Engineering Applications to Solve Organisations Issues[J]. IET Software,2020,14(2).[63]?,?. Engine Control Unit ? ? ?[J]. ,2020,47(4).[64]. Engineering - Software Engineering; Study Data from Nanjing University Update Understanding of Software Engineering (Identifying Failure-causing Schemas In the Presence of Multiple Faults)[J]. Mathematics Week,2020.[65]. Energy - Renewable Energy; Researchers from Institute of Electrical Engineering Detail New Studies and Findings in the Area of Renewable Energy (A Local Control Strategy for Distributed Energy Fluctuation Suppression Based on Soft Open Point)[J]. Journal of Mathematics,2020.[66]Ahmed Zeraoui,Mahfoud Benzerzour,Walid Maherzi,Raid Mansi,Nor-Edine Abriak. New software for the optimization of the formulation and the treatment of dredged sediments for utilization in civil engineering[J]. Journal of Soils and Sediments,2020(prepublish).[67]. Engineering - Concurrent Engineering; Reports from Delhi Technological University Add New Data to Findings in Concurrent Engineering (Systematic literature review of sentiment analysis on Twitter using soft computing techniques)[J]. Journal of Engineering,2020.[68]. Engineering; New Findings from Future University in Egypt in the Area of Engineering Reported (Decision support system for optimum soft clay improvementtechnique for highway construction projects)[J]. Journal of Engineering,2020.[69]Erica Mour?o,Jo?o Felipe Pimentel,Leonardo Murta,Marcos Kalinowski,Emilia Mendes,Claes Wohlin. On the performance of hybrid search strategies for systematic literature reviews in software engineering[J]. Information and Software Technology,2020,123.[70]. Soft Computing; Researchers from Anna University Discuss Findings in Soft Computing (A novel fuzzy mechanism for risk assessment in software projects)[J]. News of Science,2020.软件工程英文参考文献三：[71]. Software and Systems Research; New Software and Systems Research Study Results from Chalmers University of Technology Described (Why and How To Balance Alignment and Diversity of Requirements Engineering Practices In Automotive)[J]. Journal of Transportation,2020.[72]Anupama Kaushik,Devendra Kr. Tayal,Kalpana Yadav. A Comparative Analysis on Effort Estimation for Agile and Non-agile Software Projects Using DBN-ALO[J]. Arabian Journal for Science and Engineering,2020,45(6).[73]Subhrata Das,Adarsh Anand,Mohini Agarwal,Mangey Ram. Release Time Problem Incorporating the Effect of Imperfect Debugging and Fault Generation: An Analysis for Multi-Upgraded Software System[J]. International Journal of Reliability, Quality and Safety Engineering,2020,27(02).[74]Saerom Lee,Hyunmi Baek,Sehwan Oh. The role of openness in open collaboration:A focus on open‐source software development projects[J]. ETRI Journal,2020,42(2).[75]. Soft Computing; Study Results from Computer Science and Engineering Broaden Understanding of Soft Computing (Efficient attribute selection technique for leukaemia prediction using microarray gene data)[J]. Computer Technology Journal,2020.[76]. Engineering - Computational Engineering; Findings from University of Cincinnati in the Area of Computational Engineering Described (Exploratory Metamorphic Testing for Scientific Software)[J]. Computer Technology Journal,2020.[77]. Organizational and End User Computing; Data from Gyeongnam National University of Science and Technology Advance Knowledge in Organizational and End User Computing (A Contingent Approach to Facilitating Conflict Resolution in Software Development Outsourcing Projects)[J]. Computer Technology Journal,2020.[78]. Soft Computing; Findings from Department of Industrial Engineering in the Area of Soft Computing Reported (Analysis of fuzzy supply chain performance based on different buyback contract configurations)[J]. Computer Technology Journal,2020.[79]Hana Mkaouar,Bechir Zalila,Jér?me Hugues,Mohamed Jmaiel. A formal approach to AADL model-based software engineering[J]. International Journal on Software Tools for Technology Transfer,2020,22(5).[80]Riesch Michael,Nguyen Tien Dat,Jirauschek Christian. bertha: Project skeleton for scientific software.[J]. PloS one,2020,15(3).[81]. Computers; Findings from Department of Computer Sciences and Engineering Reveals New Findings on Computers (An assessment of software defined networking approach in surveillance using sparse optimization algorithm)[J]. Telecommunications Weekly,2020.[82]Luigi Ranghetti,Mirco Boschetti,Francesco Nutini,Lorenzo Busetto. “sen2r”: An R toolbox for automatically downloading and preprocessing Sentinel-2 satellite data[J]. Computers and Geosciences,2020,139.[83]Mathie Najberg,Muhammad Haji Mansor,Théodore Taillé,Céline Bouré,Rodolfo Molina-Pe?a,Frank Boury,José Luis Cenis,Emmanuel Garcion,Carmen Alvarez-Lorenzo. Aerogel sponges of silk fibroin, hyaluronic acid and heparin for soft tissue engineering: Composition-properties relationship[J]. Carbohydrate Polymers,2020,237.[84]Isonkobong Udousoro. Effective Requirement Engineering Process Model in Software Engineering[J]. Software Engineering,2020,8(1).[85]. Soft Computing; Research Conducted at Department of Computer Sciences and Engineering Has Updated Our Knowledge about Soft Computing [Hyperparameter tuning in convolutional neural networks for domain adaptation in sentiment classification (HTCNN-DASC)][J]. Network Weekly News,2020.[86]. Engineering - Software Engineering; Data on Software Engineering Discussed by Researchers at Universita della Svizzera italiana (Investigating Types and Survivability of Performance Bugs In Mobile Apps)[J]. Computer Technology Journal,2020.[87]. Engineering - Software Engineering; Findings from Nanjing University Broaden Understanding of Software Engineering (Boosting Crash-inducing Change Localization With Rank-performance-based Feature Subset Selection)[J]. Computer Technology Journal,2020.[88]. Engineering - Software Engineering; Study Data from Queen's University Belfast Update Knowledge of Software Engineering (Practical relevance of software engineering research: synthesizing the community's voice)[J]. Computer Technology Journal,2020.[89]. Engineering - Software Engineering; Researchers from Concordia University Detail New Studies and Findings in the Area of Software Engineering (MSRBot: Using bots to answer questions from software repositories)[J]. Computer Technology Journal,2020.[90]Anonymous. DBTA LIVE[J]. Database Trends and Applications,2020,34(2).[91]Tachanun KANGWANTRAKOOL,Kobkrit VIRIYAYUDHAKORN,Thanaruk THEERAMUNKONG. Software Development Effort Estimation from Unstructured Software Project Description by Sequence Models[J]. IEICE Transactions on Information and Systems,2020,E103.D(4).[92]Reza Mohammadi,Reza Javidan,Negar Rikhtegar,Manijeh Keshtgari. An intelligent multicast traffic engineering method over software defined networks[J]. Journal of High Speed Networks,2020,26(1).[93]. Engineering - Civil Engineering; Hohai University Researchers Detail New Studies and Findings in the Area of Civil Engineering (An Experimental Study on Settlement due to the Mutual Embedding of Miscellaneous Fill and Soft Soil)[J]. Journal of Engineering,2020.[94]. Engineering - Biomechanical Engineering; Researchers from Washington University St. Louis Detail New Studies and Findings in the Area of Biomechanical Engineering (Estimation of Anisotropic Material Properties of Soft Tissue By Mri of Ultrasound-induced Shear Waves)[J]. Journal of Engineering,2020.[95]. Engineering - Rock Engineering; Reports from University of Alicante Add New Data to Findings in Rock Engineering (Evaluation of Strength and Deformability of Soft Sedimentary Rocks In Dry and Saturated Conditions Through Needle Penetration and Point Load Tests: a Comparative ...)[J]. Journal of Engineering,2020.[96]. Computers; Study Findings from Department of Electrical and Communication Engineering Broaden Understanding of Computers [Improved energy efficient design in software defined wireless electroencephalography sensor networks (WESN) using distributed ...][J]. Network Weekly News,2020.[97]Mouro Erica,Pimentel Joo Felipe,Murta Leonardo,Kalinowski Marcos,Mendes Emilia,Wohlin Claes. On the Performance of Hybrid Search Strategies for Systematic Literature Reviews in Software Engineering[J]. Information and SoftwareTechnology,2020(prepublish).[98]Osuna Enrique,Rodrguez Luis-Felipe,Gutierrez-Garcia J. Octavio,Castro LuisA.. Development of computational models of emotions: A software engineering perspective[J]. Cognitive Systems Research,2020,60(C).[99]Sharifzadeh Bahador,Kalbasi Rasool,Jahangiri Mehdi,Toghraie Davood,Karimipour Arash. Computer modeling of pulsatile blood flow in elastic artery using a software program for application in biomedical engineering[J]. Computer Methods and Programs in Biomedicine,2020.[100]Shen Xiaoning,Guo Yinan,Li Aimin. Cooperative coevolution with an improved resource allocation for large-scale multi-objective software project scheduling[J]. Applied Soft Computing,2020,88(C).[101]Jung Jaesoon,Kook Junghwan,Goo Seongyeol,Wang Semyung. Corrigendum to Sound transmission analysis of plate structures using the finite element method and elementary radiator approach with radiator error index [Advances in Engineering Software 112 (2017 115][J]. Advances in Engineering Software,2020,140(C).[102]Zhang Chenyi,Pang Jun. Preface for the special issue of the 12th International Symposium on Theoretical Aspects of Software Engineering (TASE 2018[J]. Science of Computer Programming,2020,187(C).[103]Karras Oliver,Schneider Kurt,Fricker Samuel A.. Representing software project vision by means of video: A quality model for vision videos[J]. Journal of Systems and Software,2020,162(C).[104]Sutanto Juliana,Jiang Qiqi,Tan Chuan-Hoo. The contingent role of interproject connectedness in cultivating open source software projects[J]. The Journal of Strategic Information Systems,2020(prepublish).[105]Weiner Iddo,Feldman Yael,Shahar Noam,Yacoby Iftach,Tuller Tamir. CSO A sequence optimization software for engineering chloroplast expression in Chlamydomonas reinhardtii[J]. Algal Research,2020,46(C).以上就是关于软件工程英文参考文献的分享，希望对你有所帮助。

Service-oriented Communication Platform

its applications [4]. For example, the communication systems and protocols in utility facilities, which can be applied to smart grid communications, are described in the documents of IEC 61850 [5]. As for the smart grid communications, Hauser systems. Gungor
et al.
[8] showed
an end-to-end communication architecture for the smart grid. In the future, it is expected that the smart grid will be de veloped into the smart community based on the broadband ICT infrastructure. The smart community generates not only exist ing IP traffic but also real-time machine-to-machine (M2M) traffic including smart grid traffic [9]. Kim
et al.
[6] clarified communication requirements for large-scale power
et al.
[7] constructed a smart grid system us
ing wireless sensor networks (WSNs). Sauter

[IT168评测]九大主流横向扩展文件系统存储对比评测_IT168文库

【IT168 评测】对于IT主管来说，为大数据构建一个同时具有高可扩展性和成本效益的存储基础架构是非常关键的，也是必要的。

日前，Garter对目前市场主流的九大存储供应商所推出的9款横向扩展文件系统产品进行了对比评测分析，并指出了各自的有点和需改进的地方，以供用户在采购时进行对比参考，以下为报告主要内容(注：本译文部分有删减)：海量非结构化数据的存储和分析日趋重要，已经上升到战略高度，这使得在IT基础设施规划中，横向扩展存储架构将成为最突出的问题。

横向扩展存储产品往往能够实现接近线性的缩放，并通过并发来提供高性能。

大多数横向扩展存储供应商倾向于采用X86标准化硬件，从而降低硬件的采购成本，并在软件层嵌入存储信息。

横向扩展存储供应商的主要目标市场一般都是学术机构或特定行业的 HPC环境，例如基因组测序、金融建模、三维动画、气象预报和地震分析等。

因此，产品的主要关注点在于其可扩展性、原始计算能力和聚合带宽，数据保护、安全和效率则是次要考虑因素。

但是，企业对于容量空间、存储效率以及非结构化数据保护方面的需求越来越强烈，迫使供应商提供更好的安全性、可管理性、数据保护以及ISV互操作性来满足客户的需求。

虽然大多数产品用作通用存储阵列的情况还很少，但向这方面发展的趋势将会越来越明显。

IT组织必须要制定严格的规划流程来全面评估产品的关键能力以选择合适横向扩展存储供应商。

厂商需要针对特定使用情况继续优化其产品，尽管在本研究报告中，这些领先供应商兼顾到了其产品在企业环境中使用可能出现的各种情况。

但是，横向扩展存储的意识和全局命名空间在企业IT环境中并不常见，所以培训支出应该是预算分配的重要组成部分。

本研究的目的在于比较三种常见的用例——商业HPC、大的主目录以及备份和归档，并在9个关键能力方面进行考量。

非结构化数据的增长趋势明显已经超过了结构化数据。

企业和服务提供商所要求的高可扩展性和弹性存储基础设施必须在合理的成本之内，才能解决大数据的挑战，并构建云计算基础。

福建省2021届高三上学期11月英语试卷精选汇编：语法填空专题

语法填空专题福建省厦门双十中学2021届高三上学期期中考试英语试题第二节（共10小题；每小题1.5分，满分15分）阅读下面短文，在空白处填入1个适当的单词或括号内单词的正确形式。

Technological changes brought dramatic new options to Americans living in the 1990s. During this decade new forms of entertainment, commerce, research, and communication____56____(become) commonplace in the U. S. The driving force behind much of this change was an innovation ____57___(popular) known as the Internet.The Internet was developed during the 1970s by the Department of Defense. In the case of an attack, military advisers suggested __58__ advantage of being able to operate one computer from another terminal. In the early days, the Internet was used mainly by scientists to communicate with other scientists.One early problem faced by Internet users was speed. Phone ___59___(line) could only transmit information at a limited rate. The development of fiber-optic(光纤) cables allowed billions of bits of information ___60____(receive) every minute. Companies like Intel developed faster microprocessors, so personal computers could process the incoming signals more___61___(rapidly).In the early 1990s, the World Wide Web was developed, in large part, __62___ commercial purposes. Corporations created home pages ___63___ they could place text and graphics to sell products. Soon airline tickets, hotel reservations and even cars could be purchased online. Universities posted research data on the Internet, so students could find ____64____(value) information without leaving their dormitories. Companies soon discovered that work could be done at home and submitted online, so a whole new class of telecommuters began to earn a living from home offices unshaven and ___65____(wear) pajamas(睡衣).语法填空(1.5 *10=15)56.became, 57. the, 58.popularly 59. lines, 60. to be received, 61. more rapidly, 62.for, 63. where, 64. valuable, 65. wearing福州市八县（市）协作校2020-2021 学年第一学期半期联考高三英语试卷第二节语法填空（共10 小题；每小题1.5 分，满分15 分）With less people 56 (choose) to make sugar paintings, the traditional Chinese folk craft might have become a distant memory in some ways. However, a 38-year-old craftsman, Li Jiangzhong, devotes himself to 57 (keep) the art of sugar painting a live.Li worked as a miner for more than ten years. After 58 mine closed down, Li turned 59 housing decoration, until he 60 (force) to give that up due to a finger injury. Earlier this year, he discovered sugar painting, something he really had an interest in.Since there was no sugar painting craftsman in his village, he studied by 61 (he) through large quantities of videos and information on the Internet. Li loved painting when he was young, and he found it easy to learn the skill in sugar painting. He soon mastered the skill and could make 62 (vary) sugar paintings. A sugar painting is made with 63 (melt) brown or white sugar. Craftsman normally paint animals and flowers on a stone board 64 the syrup( 糖浆). When the sugar cools down, 65 appears is a piece of sugar art.第二节语法填空（共10小题；每小题1.5分，满分15分）56. choosing 57. keeping 58. the 59. to 60. was forced 61. himself 62. various 63. melted 64. with 65. what福建省龙岩市六县（市区）一中2021届高三上学期期中联考英语试题第二节（共10小题；每小题1.5分，满分15分）阅读下面短文，在空白处填入1个适当的单词或括号内单词的正确形式。

Adobe Experience Platform安全概述说明书

W H I T E P A P E RAdobe® Experience Platform Security OverviewTable of ContentsAdobe Security 3 About Adobe Experience Platform 3 Adobe Experience Platform Architecture 3 Experience Platform Security Architecture and Data Flow 5 Data Encryption 6 User Authentication for Adobe Experience Platform 6 Data Governance in Experience Platform 7 Access Control 7 Sandboxes 7 Adobe Experience Platform Hosting and Security 8 Data Center Locations 8 Disaster Recovery 8 Adobe Security Program Overview 9 The Adobe Security Organization 10 The Adobe Secure Product Lifecycle 11 Adobe Application Security 12 Adobe Operational Security 13 Adobe Enterprise Security 13 Adobe Compliance 14 Incident Response 14 Conclusion 15Adobe SecurityAt Adobe, we know the security of your digital experience is important. Security practices are deeply ingrained into our internal software development, operations processes, and tools. These practices are strictly followed by our cross-functional teams to help prevent, detect,and respond to incidents in an expedient manner. We keep up to date with the latest threatsand vulnerabilities through our collaborative work with partners, leading researchers, security research institutions, and other industry organizations. We regularly incorporate advanced security techniques into the products and services we offer.This white paper describes the defense-in-depth approach and security procedures implemented by Adobe to secure Adobe Experience Platform and its associated data. About Adobe Experience PlatformAdobe Experience Platform is an open and extensible system designed to help brandsbuild customer trust while delivering better personalized experiences. By centralizing and standardizing customer experience data and content across the enterprise, Experience Platform enables organizations to have an actionable, single view of their customer.Customer experience data can be enriched with intelligent capabilities that provide insights about customer interactions and the implications of customer engagement.Experience Platform makes the data, content, and insights available to delivery systems to act upon in real time, yielding compelling experiences at the right moment, and its robust datagovernance controls help organizations use data responsibly while delivering personalized experiences. Built on REST APIs, Experience Platform exposes the full functionality of the system to developers and partners, supporting the simple integration of enterprise solutionsand other technologies using familiar tools.Adobe Experience Platform ArchitectureAdobe Experience Platform ingests data from a variety of sources in order to help brandsbetter understand the behavior of their customers. Typical sources include enterprise data sources, including the Experience Platform customer’s own web and mobile applications,CRM and enterprise applications, cloud-based storage, and other Adobe applications.11 Source connectors, as well as ingestion run times and throughput management, are customizable in the Adobe Experience Platform UI.Using Experience Platform services, customers can structure, label, and enhance incoming data. This data is then stored in the Experience Platform data lake or profile service for analysis and use by downstream services and applications, including:• Adobe Customer Journey Analytics (CJA), Adobe Journey Optimizer (AJO), and Real-time Customer Data Platform (RT CDP), which are applications built on top of Experience Platform • Adobe Intelligent Services, including Customer AI, Attribution AI, and Content and Commerce AI, that leverage the power of artificial intelligence and machine learningin customer experience use casesExperience Platform Security Architecture and Data FlowAdobe Experience Platform ingests and exports data in the following ways:Enterprise Data Source Ingestion• Client-side Data Collection: Customer websites and mobile applications send data to the Adobe Experience Platform Edge Network for staging and preparation for ingestion.• Server-side Data Collection: Adobe Experience Cloud applications and enterprise data sources use built-in connectors to stream data directly to Experience Platform.• Adobe Experience Cloud applications as well as enterprise data sources send batch data(i.e., data collected over time) using built-in connectors.• Credentials are stored in the public cloud provider’s key vault.• If the cloud data store supports HTTPS or TLS, all data transfers between datamovement between AEP services and the cloud data store are conducted via securechannel HTTPS or TLS (1.2).• Batch Ingestion via ETL Partners: Data ingestion occurs using a non-Adobe ETL (extract, transform, and load) tool and the Experience Platform API for batch consumption. The ETL tools and the corresponding data flows reside in the customer environment.User Interactions and Admin Source Configurations• A customer’s administrators and users with appropriate access permissions can authenticate to the Experience Platform UI and configure various options for data source collection. These individuals provide credentials to connect to enterprise data sources, which are persisted in the cloud service provider’s key vaults after encrypting sensitive data.The credentials are used on the user’s behalf to create and modify data flows during design time and ingest data at run time.Access Control and Data Governance• All access to the Experience Platform data lake, whether to write new data or read existing data, is strictly controlled using the Experience Platform access control and data governance layer.Data Lake• Data is written to the appropriate location in the Experience Platform data lake for the specific customer, based on the Experience Platform data model and the configuration settings in the admin UI.Pipeline• Batch data is available by request for analysis or processing by the Experience Platform data insight and real-time customer profile services.• Streaming data is available for immediate analysis by the Experience Platform data insight and real-time customer profile services.Data Destinations• Results of analysis and processing as well as specific data sets are made available to authenticated Adobe applications, customer and partner applications, and nativeExperience Platform applications, such as Adobe Customer Journey Analytics and Adobe Journey Optimizer.• Results can also be funneled to customer-specific channels and data destinations, such as S3 buckets or social media feeds.Data EncryptionAll data in transit between Experience Platform and any external component is conducted over secure, encrypted connections using HTTPS TLS v1.2. All data at-rest is encrypted by the cloud service provider. All customer data at-rest is isolated in single-tenant cloud instances. User Authentication for Adobe Experience Platform Access to Adobe Experience Platform requires authentication with username and password. We continually work with our development teams to implement new protections based on evolving authentication standards.Users can access Experience Platform in one of three (3) different types of user-named licensing:Adobe ID is for Adobe-hosted, user-managed accounts that are created, owned, and controlled by individual users.Enterprise ID is an Adobe-hosted, enterprise-managed option for accounts that are created and controlled by IT administrators from the customer enterprise organization. While the organization owns and manages the user accounts and all associated assets, Adobe hosts the Enterprise ID and performs authentication. Admins can revoke access to Experience Platform by taking over the account or by deleting the Enterprise ID to permanently block access to associated data.Federated ID is an enterprise-managed account where all identity profiles—as well as all associated assets—are provided by the customer’s Single Sign-On (SSO) identity management system and are created, owned, controlled by the customers’ IT infrastructure.Adobe integrates with most SAML2.0 compliant identity providers. Adobe IDs and Enterprise IDs both leverage the SHA-256 hash algorithm in combination with password salts and a large number of hash iterations. Adobe continually monitors Adobe-hosted accounts for unusual or anomalous account activity and evaluates this information to help quickly mitigate threats to their security. For Federated ID accounts, Adobe does not manage the users’ passwords. More information about Adobe’s identity management services can be found in the Adobe Identity Management Services security overview.Data Governance inExperience PlatformAccess ControlAdobe Experience Platform customers can use a robust set of access control capabilities to manage access to resources and workflows. Role-based access control ensures that only authorized users can access data.Using the access control feature, Experience Platform customers can manage data usage and prevent data leakage, helping ensure regulatory compliance. Administrators benefit from a centralized administration interface to seamlessly manage permissions required for usersto access sandboxes and specific workflows, including data ingestion, data modeling, data management, profile management, identity management, and destinations. SandboxesIn Adobe Experience Platform, customer data is contained within sandboxes, or virtual partitions within a single Experience Platform instance. These sandboxes are shared across Experience Platform services and applications and provide operational and data isolation to support market, brand, or initiative-focused marketing and digital experience operations.Adobe provides two types of sandboxes to support software development lifecycle requirements: development and production. Experience Platform supports multiple production and development sandboxes, with each sandbox maintaining its own independent library of Experience Platform resources, including schemas, datasets, and profiles. Content and actions taken within any given sandbox are confined only to that sandbox and do not affect any other sandboxes.For more information about Adobe Experience Platform data governance, please see the Adobe Experience Platform Data Governance white paper.Adobe Experience Platform Hosting and SecurityData Center LocationsThe Adobe Experience Platform service infrastructure resides in enterprise-class data centers from public cloud service providers in U.S. East (Virginia), Amsterdam (NL), and Sydney (AU). Upon provisioning, customers can designate the regional data center(s) where the data ingested into Experience Platform will be sent for storage.AmsterdamVirginiaSydney Figure 2: Adobe Experience Platform Data Center LocationsDisaster RecoveryAdobe Experience Platform uptime data is available on the Adobe Status website. Additionally, for both planned and unplanned system downtime, the Experience Platform team follows a notification process to inform customers about the status of the service. If there is a need to migrate the operational service from a primary site to a disaster recovery site, customers will receive several specific notifications including:• Notification of the intent to migrate the services to the disaster recovery site• Hourly progress updates during the service migration• Notification of completion of the migration to the disaster recovery siteThe notifications will also include contact information and availability for client support and customer success representatives. These representatives will answer questions and concerns during the migration as well as after the migration to promote a seamless transition to newly active operations on a different regional site.Adobe Security Program Overview• Application Security — Focuses on the security of our product code, conducts threat research, and implements bug bounty.• Operational Security — Helps monitor and secure our systems, networks, and production cloud systems.• Enterprise Security — Concentrates on secure access to and authentication for the Adobe corporate environment.• Compliance — Oversees our security governance model, audit and compliance programs, and risk analysis; and• Incident Response — Includes our 24x7 security operations center and threat responders. Illustrative of our commitment to the security of our products and services, the centers of excellence report to the office of the Chief Security Officer (CSO), who coordinates all current security efforts and develops the vision for the future evolution of security at Adobe.The Adobe Security OrganizationBased on a platform of transparent, accountable, and informed decision-making, the Adobe security organization brings together the full range of security services under a single governance model. At a senior level, the CSO closely collaborates with the Chief Information Officer (CIO) and Chief Privacy Officer (CPO) to help ensure alignment on security strategy and operations.In addition to the centers of excellence described above, Adobe embeds team members fromin-depth ‘martial arts’-styled training program, which is tailored to their specific roles. For more information on our culture of security and our training programs, please see the Adobe Security Culture white paper.The Adobe Secure Product Lifecycle Integrated into several stages of the product lifecycle—from design and development to quality assurance, testing, and deployment— the Adobe Secure Product Lifecycle (SPLC) is the foundation of all security at Adobe. A rigorous set of several hundred specific security activities spanning software development practices, processes, and tools, the Adobe SPLC defines clear, repeatable processes to help our development teams build security into our products and services and continuously evolves to incorporate the latest industry best practices.Deployment Staging & Stabilization Development & TestingRequirements& PlanningDesignFigure 5: The Adobe Secure Product LifecycleAdobe maintains a published Secure Product Lifecycle Standard that is available for review upon request. More information about the components of the Adobe SPLC can be found in the Adobe Application Security Overview.At Adobe, building applications in a “secure by default” manner begins with the Adobe Application Security Stack. Combining clear, repeatable processes based on established research and experience with automation that helps ensure consistent application of security controls, the Adobe Application Security Stack helps improve developer efficiency and minimize the risk of security mistakes. Using tested and pre-approved secure coding blocks that eliminate the need to code commonly used patterns and blocks from scratch, developersthose for work specific to our use of Amazon Web Services (AWS) and Microsoft Azure public cloud infrastructure. These standards are available for view upon request.For more information on Adobe application security, please see the Adobe Application Security Overview.To help ensure that all Adobe products and services are designed from inception with security best practices in mind, the operational security team created the Adobe Operational Security Stack (OSS). The OSS is a consolidated set of tools that help product developers and engineers improve their security posture and reduce risk to both Adobe and our customers while also helping drive Adobe-wide adherence to compliance, privacy, and otherAdobe Enterprise SecurityIn addition to securing our products and services as well as our cloud hosting operations, Adobe also employs a variety of internal security controls to help ensure the security ofour internal networks and systems, physical corporate locations, employees, and our customers’ data.For more information on our enterprise security controls and standards we have developed for these controls, please see the Adobe Enterprise Security Overview.Adobe ComplianceAll Adobe products and services adhere to the Adobe Common Controls Framework (CCF),a set of security activities and compliance controls that are implemented within our product operations teams as well as in various parts of our infrastructure and application teams.As much as possible, Adobe leverages leading-edge automation processes to alert teams to possible non-compliance situations and help ensure swift mitigation and realignment. Adobe products and services either meet applicable legal standards or can be used in away that enables customers to help meet their legal obligations related to the use of service providers. Customers maintain control over their documents, data, and workflows, and can choose how to best comply with local or regional regulations, such as the General Data Protection Regulation (GDPR) in the EU.Adobe also maintains a compliance training and related standards that are available for review upon request. For more information on the Adobe CCF and key certifications, please see the Adobe Compliance, Certifications, and Standards List.Incident ResponseAdobe strives to ensure that its risk and vulnerability management, incident response, mitigation, and resolution processes are nimble and accurate. We continuously monitor the threat landscape, share knowledge with security experts around the world, swiftly resolve incidents when they occur, and feed this information back to our development teams to help achieve the highest levels of security for all Adobe products and services.We also maintain internal standards for incident response and vulnerability management that are available for view upon request. For more detail on Adobe’s incident response and notification process, please see the Adobe Incident Response Overview.ConclusionThe proactive approach to security and stringent procedures described in this paper help protect the security of Adobe Experience Platform and your confidential data. At Adobe, we take the security of your digital experience data very seriously and we continuously monitor the evolving threat landscape to try to stay ahead of malicious activities and help ensure the security our customers’ data.For more information on:Adobe security: /securityInformation in this document is subject to change without notice. For more information on Adobe solutions and controls, please contact your Adobe sales representative. Further details on the Adobe solution, including SLAs, change approval processes, access control procedures, and disaster recovery processes are available.Adobe345 Park AvenueSan Jose, CA 95110-2704USA 。

超微主板IPMI 操作手册

SUPERMICRO BMC_IPMI 操作手册BMC：基板管理控制器 (Baseboard Management Controller)BMC（Baseboard Management Controller，基板管理控制器）支持行业标准的 IPMI 规范。

IPMI规范描述了已经内置到主板上的管理功能。

总的来说，BMC就是为远程管理接口IPMI提供硬件支持。

IPMI：智能型平台管理接口（Intelligent Platform Management Interface）。

IPMI定义了嵌入式管理子系统进行通信的特定方法。

IPMI 信息通过基板管理控制器 (BMC)（位于 IPMI 规格的硬件组件上）进行交流。

使用低级硬件智能管理而不使用操作系统进行管理。

是管理基于 Intel结构的企业系统中所使用的外围设备采用的一种工业标准。

用户可以利用IPMI监视服务器的物理健康特征，如温度、电压、风扇工作状态、电源状态等。

一、获取IPMI的IP地址。

1、用IPMICFG软件获取。

可用IPMICFG在DOS命令行下获取、配置IP等信息。

2、或者进入主板BIOS获取在“Advance选项____IPMI Configuration 子项下”、配置IP。

1）、在子项下 Set LAN Configuration；2）、可通过DHCP服务来自动获取3）、可通过手动设置；4）、通过管理端网络链接使用IE浏览器6.0以上版本访问。

二、进入管理界面。

首先安装jre-6u7-windows-i586-p-s或更高版本JAVA工具。

可在SUN官方网站上下载，地址：/javase/downloads/index.jsp；IE6.0以上版本浏览器。

条件不达到有可能IPMI无法正常使用。

将管理端的IP地址和IPMI的IP地址配置成统一网段。

连接上网线至IPIM的专用网口或者主板的第一个网口。

在IE浏览器输入IPMI的IP地址，即显示出管理界面。

set_ideal_network -no_propagation用法 -回复

set_ideal_network -no_propagation用法-回复“set_ideal_network no_propagation用法”指的是在构建神经网络时，采用了无传播(no propagation)的理想网络(ideal network)设置。

本文将逐步介绍这个用法，包括它的定义、作用、使用场景以及具体操作步骤。

通过阐述这些内容，读者将能够深入了解set_ideal_networkno_propagation的用途和实践方法。

一、定义与作用在开始之前，我们先来了解“set_ideal_network no_propagation”的定义和作用。

set_ideal_network是指在神经网络构建的过程中，创建一个没有传播的理想网络。

no_propagation表示网络在训练过程中不进行传播，即不计算梯度。

这种设置的主要作用是为了比较网络在完全无传播的情况下的性能表现。

通过与其他传统的网络进行对比，可以验证传统网络中传递的信息对模型的训练和性能产生的影响。

二、使用场景接下来，我们来探讨一些使用set_ideal_network no_propagation的典型场景。

这种设置通常用于以下情况：1. 网络设计和优化：理想网络的设置可以帮助设计者更好地理解各个网络层级之间的复杂关系，提供参考用于优化网络的结构和参数。

2. 梯度分析和网络评估：通过与传统网络进行比较，可以分析并评估梯度对训练过程和模型效果的影响。

这有助于深入研究网络的训练机制以及隐藏层间的信息流动。

3. 比较不同模型结构：使用理想网络进行比较可以帮助研究人员更好地理解不同模型结构对学习能力和权重更新的影响，进而优化网络架构。

三、操作步骤现在，我们将介绍一些具体的操作步骤来使用set_ideal_networkno_propagation。

1. 定义理想网络：首先，我们需要定义一个不进行传播的理想网络。

这可以通过在定义网络模型时注意不进行反向传播来实现。

set_ideal_network -no_propagation用法 -回复

set_ideal_network -no_propagation用法-回复set_ideal_network 是一个用于设置网络的理想传播模式的函数。

该函数可以用于优化和调整网络节点之间的连接方式，以实现更高效、更可靠的数据传输和通信。

传统的网络传播模式存在一些缺陷和局限性，例如信号衰减、干扰、延迟等问题，这些问题可能导致数据传输的错误和不稳定性。

为了解决这些问题，研究人员提出了一种新的传播模式——no_propagation（无传播）。

no_propagation 模式的基本原理是消除传播过程中产生的影响和干扰，使数据能够直接从发送节点到达接收节点，从而提高数据传输的效率和成功率。

下面将详细介绍set_ideal_network 函数的用法，并讨论它在不同领域的应用。

首先，我们需要了解set_ideal_network 函数的基本调用方法。

该函数接受一个参数，即网络对象的引用，通过修改网络对象的属性来设置传播模式。

以下是函数的调用示例：pythonset_ideal_network(network)接下来，我们将一步一步地回答set_ideal_network 函数的用法以及它在不同领域的应用。

一、理论基础no_propagation 模式的原理基于无线通信中的近场传输原理。

传统的无线通信模式中，信号会通过空气或其他介质的传播而衰减、受到干扰，从而导致数据传输的不可靠性。

而no_propagation 模式通过直接将信号传递到接收节点，避免了传播过程中的干扰，从而提高了数据传输的效率和可靠性。

二、函数参数说明set_ideal_network 函数需要一个网络对象的引用作为参数。

网络对象是一个数据结构，用于表示网络中的节点和它们之间的连接关系。

通过修改网络对象的属性，我们可以对网络的连接方式进行调整。

三、函数实现步骤set_ideal_network 函数的实现步骤如下：1. 创建一个空的理想网络对象。

PyTorch 安装指南：Jetson 平台说明书

Installing PyTorch For Jetson PlatformInstallation GuideTable of Contents Chapter 1. Overview (1)1.1. Benefits of PyTorch for Jetson Platform (1)Chapter 2. Prerequisites and Installation (3)2.1. Installing Multiple PyTorch Versions (3)2.2. Upgrading PyTorch (4)Chapter 3. Verifying The Installation (5)Chapter 4. Uninstalling (6)Chapter 5. Troubleshooting (7)Chapter 1.OverviewPyTorch on Jetson PlatformPyTorch (for JetPack) is an optimized tensor library for deep learning, using GPUs and CPUs. Automatic differentiation is done with a tape-based system at both a functional and neural network layer level. This functionality brings a high level of flexibility, speed as a deep learning framework, and provides accelerated NumPy-like functionality. These NVIDIA-provided redistributables are Python pip wheel installers for PyTorch, with GPU-acceleration and support for cuDNN. The packages are intended to be installed on top of the specified version of JetPack as in the provided documentation.Jetson AGX XavierThe NVIDIA Jetson AGX Xavier developer kit for Jetson platform is the world's first AI computer for autonomous machines. The Jetson AGX Xavier delivers the performance of a GPU workstation in an embedded module under 30W.Jetson AGX OrinThe NVIDIA Jetson AGX Orin Developer Kit includes a high-performance, power-efficient Jetson AGX Orin module, and can emulate the other Jetson modules. You now have up to 275 TOPS and 8X the performance of NVIDIA Jetson AGX Xavier in the same compact form-factor for developing advanced robots and other autonomous machine products. Jetson Xavier NXThe NVIDIA Jetson Xavier NX brings supercomputer performance to the edge in a small form factor system-on-module. Up to 21 TOPS of accelerated computing delivers the horsepower to run modern neural networks in parallel and process data from multiple high-resolution sensors — a requirement for full AI systems.1.1. Benefits of PyTorch for JetsonPlatformOverview Installing PyTorch for Jetson Platform provides you with the access to the latest version of the framework on a lightweight, mobile platform.Chapter 2.Prerequisites andInstallationBefore you install PyTorch for Jetson, ensure you:1.Install JetPack on your Jetson device.2.Install system packages required by PyTorch:sudo apt-get -y update;sudo apt-get -y install autoconf bc build-essential g++-8 gcc-8 clang-8 lld-8 gettext-base gfortran-8 iputils-ping libbz2-dev libc++-dev libcgal-dev libffi-dev libfreetype6-dev libhdf5-dev libjpeg-dev liblzma-dev libncurses5-dev libncursesw5-dev libpng-devlibreadline-dev libssl-dev libsqlite3-dev libxml2-dev libxslt-dev locales moreutils openssl python-openssl rsync scons python3-pip libopenblas-dev;Next, install PyTorch with the following steps:1.Export with the following command:export TORCH_INSTALL=https:///compute/redist/jp/v511/pytorch/ torch-2.0.0+nv23.05-cp38-cp38-linux_aarch64.whlOr, download the wheel file and set.export TORCH_INSTALL=path/to/torch-2.0.0+nv23.05-cp38-cp38-linux_aarch64.whl2.Install PyTorch.python3 -m pip install --upgrade pip; python3 -m pip install aiohttp numpy=='1.19.4' scipy=='1.5.3' export "LD_LIBRARY_PATH=/usr/lib/llvm-8/lib:$LD_LIBRARY_PATH"; python3 -m pip install --upgrade protobuf; python3 -m pip install --no-cache $TORCH_INSTALLIf you want to install a specific version of PyTorch, replace TORCH_INSTALL with:https:///compute/redist/jp/v$JP_VERSION/pytorch/ $PYT_VERSIONWhere:JP_VERSIONThe major and minor version of JetPack you are using, such as 461 for JetPack 4.6.1 or 50 for JetPack 5.0.PYT_VERSIONThe released version of the PyTorch wheels, as given in the Compatibility Matrix. 2.1. Installing Multiple PyTorch VersionsPrerequisites and Installation If you want to have multiple versions of PyTorch available at the same time, this can be accomplished using virtual environments. See below.Set up the Virtual EnvironmentFirst, install the virtualenv package and create a new Python 3 virtual environment: $ sudo apt-get install virtualenv$ python3 -m virtualenv -p python3 <chosen_venv_name>Activate the Virtual EnvironmentNext, activate the virtual environment:$ source <chosen_venv_name>/bin/activateInstall the desired version of PyTorch:pip3 install --no-cache https:///compute/redist/jp/v51/pytorch/ <torch_version_desired>Deactivate the Virtual EnvironmentFinally, deactivate the virtual environment:$ deactivateRun a Specific Version of PyTorchAfter the virtual environment has been set up, simply activate it to have access to the specific version of PyTorch. Make sure to deactivate the environment after use:$ source <chosen_venv_name>/bin/activate$ <Run the desired PyTorch scripts>$ deactivate2.2. Upgrading PyTorchTo upgrade to a more recent release of PyTorch, if one is available, uninstall the current PyTorch version and refer to Prerequisites and Installation to install the new desired release.Chapter 3.Verifying The InstallationAbout this taskTo verify that PyTorch has been successfully installed on the Jetson platform, you’ll need to launch a Python prompt and import PyTorch.Procedure1.From the terminal, run:$ export LD_LIBRARY_PATH=/usr/lib/llvm-8/lib:$LD_LIBRARY_PATH$ python32.Import PyTorch:>>> import torchIf PyTorch was installed correctly, this command should execute without error.Chapter 4.UninstallingPyTorch can easily be uninstalled using the pip3 uninstall command, as below: $ sudo pip3 uninstall -y torchChapter 5.TroubleshootingJoin the NVIDIA Jetson and Embedded Systems community to discuss Jetson platform-specific issues.NoticeThis document is provided for information purposes only and shall not be regarded as a warranty of a certain functionality, condition, or quality of a product. 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AIStation 人工智能平台用户指南说明书

Artificial intelligence development platformRelease AI computing power, accelerate intelligent evolutionAI&HPCAIStation -Artificial Intelligence PlatformUser DataUtilizationTraining40%→80%2 days →4 hrs.Telecom FinanceMedicalManuf.Trans.Internet Development & TrainingDeployment & InferenceDeployment2 days →5 minDataModelServingLaptopMobileIndustryRobotIoTPyTorch Caffe MxNetPaddlePaddle TensorFlow ImportPre-processing accelerationTraining Visualization Hyper-para. tuningOn-demandAuto sched.OptimizationJupyter WebShell PipelineData mgnt computing resources Dev. ToolsModelTensorFlow ServingTensorRT Inference Server PyTorch Inference ServerServingDeployingDev. Tools PipelineData processing RecommendationsystemCV NLPScenarioOn-demand Auto sched.Optimization"Efficiency" has become a bottleneck restricting the development of enterprise AI businesspycharmjupyterVstudiosublime70%50%70%Data security issuesInefficient collaborative developmentLack of centralized data management Low resource utilizationInconvenient for large-scale trainingDecentralized Resource Management Lack of synergy in R&D, slow business responseR&D lacks a unified processAIStation –Artificial intelligence development platformTensorflow Pytorch Paddle Caffe MXNetAIStation Integrated development platformModel DevelopmentBuild environment Model Debugging Model OptimizationModel Deployment Model Loading Service DeploymentAPI ServiceModel Training Task Queuing Distributed Training Visual AnalysisAI computing resourcesTraining samplesApplication stackCPU GPUNFS BeeGFS HDFSComputing Resource Pooling User Quota management Utilizing GPU usagePool schedulingData accelerationAutomated DDP trainingSSDResource poolData pooldata1data2data3node1node2node3data4data5Dataset managementData pre-loading Cached data managementSolving data IO bottleneck Accelerating large scale dataset transferring and model trainingLow threshold for DDP training Helping developers drive massive computing power to iteratively trainmodelsbatch2batch1batch0Data loadingbatch3BeeGFSwork2GPU serverworker1GPUserverworker0GPUserverwork3GPU ServerAIStation TensorFlowCustomized MPI operatorsHighlighted featuresSSDSSDGPU GPU GPU GPU GPUGPUGPUGPUGPU Cards MIG instancesResource PoolingUser QuotaUser QuotaA I St a t i o n d e ve l o p m e n t P l a t f o r m A rc h i te c t u reP100V100V100sA100A30… …Ethernet ClusterInfiniband ClusterRoCE ClusterStorageNFS 、BeeGFS 、Lustre 、HDFS 、Object StorageLinux OSNVIDIA driver package: GPU, Mallanox NIC, etcOperating SystemHardware ClusterNVIDIAGPU seriesMonitoringSchedulingGPU PluginOperatorKubernetes + dockerNetwork PluginSRIOV PluginMultus CNIData prep.Algorithm prototype TrainingTestResource Enginedata mgmtJupyterimage mgmtwebshell/ssh multi-instance visualizationquota mgmtresource mgmt deployment job workflowmgmt job lifecycleproject mgmtalgorithm mgmtmodel mgmt Report HAMulti-tenant System settingBusiness ManagementAuthenticationAPIsAI Application Development3rd or user-defined system integrationDeployment ModeComputing Nodes Storage ：SSD 2T-10TGPU ：8*V100Management network Ethernet @ 1/10Gbps IPMIEthernet @ 1GbpsManagement Node Storage size ：4T-100TCluster Size （10-80persons ）ManagerDeployment Mode （Larger Scale+HA ）Storage 100T-200TManagement network Ethernet @ 1/10Gbps IPMIEthernet @ 1Gbps Management Node 1*Main ，2*BackupCluster Size （10-80persons ）Computing NodesSSD 2T-10T 8*V100Computing NodesSSD 2T-10T 8*V100Manager...I00G EDR infiniband EDR@100GpsOne-stop service full-cycle management,Easy use for distributed trainingHelping developers drive massive computing powerto iteratively train modelsOne-stop AI Dev. platformAI framework AI ops tools GPU driver & Cuda GPUStandard interface for AI Chips Multiply AI Chips supportedHeterogeneousComprehensive resource using statisticsData security and access control Automatic faulty analysis and solutionsIntelligent maintenance & securityHighlighted featuresAIStationStandard and unifiedManagementPollingSchedulingCPU GPU FPGAASICA100A30A40V100MLU270MLU390Cloud AIC 100•Personal data isolation•Collaborative sharing of public data •Unified data set managementC e n t r a l i z e d d a t a m a n a g e m e n tf a c i l i t a t e c o l l a b o r a t i v e d e v e l o p m e n t •Dataset preloading •Data Affinity Scheduling•Local cache management strategyD a t a c a c h e a c c e l e r a t i o ne f f e c t i v e l y s o l v e I /O b o t t l e n e c k s AIStation –Data Synergy Acceleration•Data access control•Data security sandbox, anti-download •Multiple copies ensure secure data backupS e c u r i t y p o l i c yUser DataTraining SamplesSharing Data（NFS 、HDFS 、BeeGFS 、Cloud Storage ）D a t a M a n a g e m e n t ：M u l t i -s t o r a g e Sy s t e m•Support “main -node ”storage using mode ；•Unified access and data usage for NFS 、BeeGFS 、HDFS 、Lustre through UI;•Built-in NFS storage supports small file merger and transfer, optimizing the cache efficiency of massive small filesAIStationComputing PoolStorage extension （storage interface 、data exchange ）Data accelerationMain storageSSD+BeeGFSNode Storage（NFS ）Node Storage（HDFS ）Node Storage（Lustre ）Data exchangeGPU PoolAIStationUser01UserNcaffeTensorflowmxnetpytorchGPUGPU GPU GPU GPUGPUGPUGPUGPU GPU GPU GPU GPUGPUGPUGPUGPU GPU GPU GPU GPUGPUGPUGPUGPU GPU GPU GPU GPUGPUGPUGPUAIStation –Resource SchedulingR e s o u r c e a l l o c a t i o n m a n a g e m e n tUser GPU resource quota limit User storage quota limitResource partition: target users, resource usageF l e x i b l e r e s o u r c e s c h e d u l i n g•Network topology affinity scheduling •PCIE affinity scheduling•Device type affinity scheduling •GPU fine-grained distributionD y n a m i c s c h e d u l i n g•Allocate computing resources on demand •Automatically released when task completedG P U M a n a g e m e n t ：F i n e g r a n u l a r i t y G P U u s i n guser1user2user3user4user2481632123456GPU mem （G ）Time （H ）user1user2IdleIdle481632123456GPU mem （G ）Time （H ）GPU sharing scheduling policy based on CUDA to realize single-card GPU resource reuse and greatly improve computing resource utilization.Elastic sharing:Resources are allocated based on the number of tasks to be multiplexed.A single card supports a maximum of 64tasks to be multiplexed.Strict sharing:the GPU memory is isolated and allocated in any granularity (minimum:1GB).and resources are isolated based on thegraphics memory ；Flexible and convenient:user application to achieve "zero intrusion",easy application code migration ；S c h e d u l i n g w i t h M I G8 * A100 GPUsN V I D I A A100M I G s u p p o r t i n gUtilizing GPU usage• A single A100 GPU achieves up to 7x instance partitioning and up to56x performance on 8*A100 GPUs in Inspur NF5488A5;•Allocates appropriate computing power resources to tasks withdifferent load requirements.•Automatic MIG resource pool management, on-demand application,release after use;Convenient operation and maintenance•Set different sizes of pre-configured MIG instance templates.•Standard configuration UI for IT and DevOps team.•Simplify NVIDIA A100 utilization and resource management;56 *MIG instancesRe s o u rc e m a n a g e m e n t ：N U M A ba s e d s c h e d u l i n gKubeletResource management PluginInspur-DevicePluginGPUGPU topo scoreGPU resource updateGPU allocatingAIStation SchedulerGPU allocationAutomatically detects the topology of compute nodes and preferentially allocates CPU and GPU resources in the same NUMA group to a container to make full use of the communication bandwidth in the groupAIStation –Integrated AI training frameworkPrivate image library PublicimagelibraryinspurAIimagelibraryAI DevelopmentFrameworkAI Developmentcomponents and toolsGPU Driver anddevelopment libraryGPU computingresources◆Te n s o r f l o w,P y t o r c h,P a d d l e,C a f f e,M X N e t◆B u i l d a p r i v a t e w a r e h o u s e t o m a n a g et h e A I a p p l i c a t i o n s t a c k◆S u p p o r t i m a g e c r e a t i o n a n d e x t e r n a li m p o r t◆S u p p o r t o p e n s o u r c e r e p o s i t o r i e s s u c ha s N G C a n d D o c k e r H u b◆B u i l t-i n m a i n s t r e a m d e v e l o p m e n t t o o l sa n d s u p p o r t d o c k i n g w i t h l o c a l I D E•Built-in Jupyter and Shell tools •Support docking with local IDE •Support command line operationQuickly enterdevelopment mode•Allocate computing resources on demand•Quick creation through the interface•Rapid Copy Development EnvironmentRapid build Model Development Environment•Life cycle management •Real-time monitoring of resource performance•One-click submission of training tasksCentralized management of development environmentQuickly build development environment, focus on model developmentD e ve l o p m e n t P l a t f o r mJupyterWebShell本地IDEDevelopment PlatformDev. Platform StatusDevelopment environment instancemonitoring The development environment saves the imageS e c o n d l e v e l b u i l d•On –demand GPU ；•T ensorflow/MXNet/Pytorch/Caffe ；•Single-GPU, multi-GPU, distributed training ；•Flexible adjustment of resources on demand decouples the binding of runtime environment and computing power ；I n t e r a c t i v e m o d e l i n g •Jupyter / WebShell / IDE V i s u a l i z a t i o nT ensorBoard / Visdom / NetscopeF u l l c y c l e m a n a g e m e n t S t a t u smonitoring/Performance monitoring/Port password memoryImage save/copy expansion/start/delete etcVisualizationTensorboardVisdom NetscopeEnhanced affinity scheduling, optimized distributed scheduling strategy, multi-GPU training acceleration ratio can reach more than 90%.Optimized most of the code based on open source;Fixed a bug where workers and launchers could not start at the same time;Task status is more detailed.•Supports distributed training for mainstream frameworks•Provides one-page submission and command line submission of training tasks.M u l t i p l e w a y s t o s u p p o r t d i s t r i b u t e dQ u i c k s t a r t d i s t r i b u t i o nI m p r o v e c o m p u t i n g p e r f o r m a n c eDistributed task scheduling to speed up model trainingAIStation –Training ManagementAIStation –Resource MonitoringO v e r a l l M o n i t o r i n g•Usage status of cluster resources such as GPU, CPU, and storage •Computing node health and performance•User task status and resource usageR e s o u r c e U s a g e St a t i s t i c s•Cluster-level resource usage statistics•Cluster-level task scale statistics•User-level resource usage statistics•User-level task scale statisticsS y s t e m A l a r m•hardware malfunction•System health status•Computing resource utilizationM u l t i -te n a n t M a n a g e m e n tAIStationUserUser2User group1User group2Kubernetes Namespace1Namespace2Cluster resource ☐Supports an administrator -Tenant administrator -Common User organization structure. Tenant administrators can conveniently manage user members and services in user groups, while platformadministrators can restrict access to and use of resources and data in user groups.☐User authentication: LDAP as user authentication system, supporting third-party LDAP/NIS systems.☐Resource quotas control for users and user groups using K8S namespace.☐User operations: Users can be added, logged out, removed, and reset passwords in batches. Users can be enabled or disabled to download data and schedule urgent tasks.I n t e l l i g e n t p l a t f o r m o p e r a t i o n a n d m a i n t e n a n c eIntelligent diagnosis and recovery tool•Based on the existing cluster monitoring, alarm and statistics functions, the operation monitoring component is removed to support independent deployment and use;•Health monitoring: Obtain the status and list display (monitoring information and abnormal events display) of components (micro-services and NFS).•Abnormal repair: Based on the operation and maintenance experience of AIStation, automatic or manual repair of the sorted events such as interface timeout and service abnormalities (microservice restart and NFS remount);Intelligent fault toleranceSupports active and standby management node health monitoring, HA status monitoring, and smooth switchover between active and standby management nodes without affecting services. Monitors alarms forabnormal computenode resource usage toensure the smoothrunning of computenodes.In the event of a systemfailure, the training taskautomatically startssmooth migrationwithin 30 secondsMonitor the status ofkey services andabnormal warning toensure the smoothoperation of user coreservices.M a n a g e m e n t n o d e h i g h l y a v a i l a b l e C o m p u t i n g n o d eF a u l t t o l e r a n c eC r i t i c a l s e r v i c e sf a u l t t o l e r a n tTr a i n i n g m i s s i o nf a u l t t o l e r a n c eN o r t h b o u n d i n t e r f a c e•Secure, flexible, extensible northbound interface based on REST APIs.AIStationQuery URL Status Usages Performance status logs performance resultsReturn URL resource framework scripts dataset environment Login info performance resource framework dataset Return URL Query URL Query URL Return URL monitordeveloping training Computing resourcesDatasets Applications Caffe TensorFlow Pytorch Keras MXNet theanodata1data2data3data4data5AIStation product featuresFull AI business process support Integrated cluster management Efficient computing resource scheduling Data caching strategy reliable security mechanismsUse Case ：Automatic driveSolutions:•Increasing computing cluster resource utilization by 30% with efficient scheduler.•One-stop service full-cycle management,streamlined deployments.•Computing support, data management.Background ：•Widely serving the commercial vehicle and passenger vehiclefront loading market. •The company provides ADAS and ADS system products andsolutions, as well as high-performance intelligent visualperception system products required for intelligent driving.U s e C a s e ：c o m m u n i c a t i o n s te c h n o l o g y c o m pa n y•Quick deployment and distributed •GPU pooling •Huge files reading and training optimizationBackground•HD video conference and mobile conference are provided,and voice recognition and visual processing are the main scenarios.•Increased scale of sample data,distributed training deployment and management,a unified AI development platform is required to support the rapid development of service.ProblemsSolutions •Increasing size of dataset (~1.5T), distributed training;•GPU resource allocating automatically ；•Efficient and optimized management for the huge set of small files ；Use Case: Build One-Stop AI Workflow for Largest Excavator Manufacturer Revenue 15.7B$ExcavatorsPer Year 100,000+Factories 30+AIStation built one-stop AI workflow to connect cloud, edge,and localclusters; support 75 production systems.API Calls Per day 25 M QoS 0missper 10M calls Model Dev Cycle 2 weeks -> 3days Use AI to automate 90% production lines, double production capacity.SANY HEAVY INDUSTRY CO., LTDSANY CloudAIStationModel Dev &Training Inference ServiceSensor Data Data Download Realtime work condition analysis Inference API invoke Training Cluster Inference ClusterTraining Jobs InferenceServices200 * 5280M5 800 * T4, inference; 40* 5468M5 320 * V100, training。

基于物联网的LED智慧照明系统研究

摘要随着物联网技术的广泛应用，对于网络数据的存储和传输安全有迫切的需求。

智慧照明系统作为物联网的一个应用，其在数据存储和通信安全的重要性也日益显现，区块链技术由于其在数据存储、网络通信等方面具有极高的安全性，将区块链技术应用与物联网照明中，是解决网络安全问题的一个有效途径。

本文研究了一种基于区块链技术的照明物联网管控系统，该系统能保障物联网照明系统的网络安全，且将轻量级的物联网设备独立于区块链之外，提高数据处理的实时性，同时利用智能合约实现照明的智能控制。

论文主要工作如下：（1）根据物联网照明的功能需求，提出了物联网照明的系统架构，在架构的基础上，提出了基于以太坊网络平台的可伸缩管控机制，该管控机制建立于的以太坊区块链平台，通过研究将控制策略、设备管理、访问控制写入智能合约的方法，实现对照明的智能控制、设备的安全管理、数据的访问控制，同时对数据信息进行安全存储。

（2）针对轻量级设备接入物联网难度大的问题，本文设计了采用NB_IoT技术且依靠CoAP协议实现入网的物联网设备。

该设备兼ZigBee、WiFi、NB_IoT三种通信方式于一体，可实现照明系统的异架构组网，其以嵌入式微处理器STM32F107为核心，采用NB_IoT技术进行入网连接，实现远程控制，通过ZigBee 实现与下级设备的自组网，利用WiFi实现本地控制。

（3）设计了LED驱动控制器，该驱动控器以嵌入式微处理器STM32F103为核心，利用ZigBee进行无线通信，实现与上级设备的自组网，兼具开关、调光和电功量检测功能。

（4）利用Web技术，开发了应用软件。

该应用软件可通过API接口与物联网平台进行数据交互，获取数据信息，具有数据处理分析能力，能够对照明系统进行状态监控、故障报警。

（5）通过单元测试和联调测试两种方法，对系统进行功能和性能测试，结果表明，在控制命令的正确执行率、量测数据的准确率和响应命令的延时性均达到了预期的目标。

智能制造单元系统集成应用实训平台的设计与实现

实验技术与管理第37卷第8期 2020年8月Experimental Technology and Management Vol.37 No.8 Aug. 2020ISSN 1002-4956 CN11-2034/TDOI: 10.16791/ki.sjg.2020.08.050智能制造单元系统集成应用实训平台的设计与实现许怡赦1,2，罗建辉1，李铭贵3（1. 湖南机电职业技术学院电气工程学院，湖南长沙 415101；2. 湖南广播电视大学网络技术学院，湖南长沙 410004；3. 北京华航唯实机器人科技股份有限公司，北京 100080）摘要：针对智能制造类专业综合实训教学需求，设计了智能制造单元系统集成应用实训平台。

该平台集成智能仓储物流、工业机器人、数控加工、智能检测等模块，利用物联网、工业以太网实现信息互联，融入MES 系统实现数据采集与可视化，接入云服务实现一体化联控。

基于该平台设计了硬件搭建及电气接线、通信组态及调试、各单元智能化改造和控制网络集成调试4个由易到难的实训项目，还在此基础上拓展设计了创新性、开发性实训项目，夯实了学生的知识基础，提升了学生的综合能力、创新开发能力及职业素养。

关键词：智能制造；实训平台；智能化改造；实训教学中图分类号：TH165；G482 文献标识码：A 文章编号：1002-4956(2020)08-0227-06Design and realization of integrated application training platform forintelligent manufacturing unit systemXU Yishe 1,2, LUO Jianhui 1, LI Minggui 3(1. College of Electrical Engineering, Hunan Mechanical and Electrical Polytechnic, Changsha 415101, China;2. College of Network Technology, Hunan Radio and TV University, Changsha 410004, China;3. Beijing C.H.L. Robotics Co., Ltd., Beijing 100080, China)Abstract: According to the teaching needs of the comprehensive training of intelligent manufacturing specialty, the integrated application training platform of intelligent manufacturing unit system is designed. The platform integrates intelligent warehousing and logistics, industrial robots, numerical control processing, intelligent detection and others, uses the Internet of things and industrial Ethernet to realize information interconnection, incorporates MES system to achieve data collection and visualization, and connects cloud services to achieve integrated joint control. Based on the platform, four easy-to-difficult training projects are designed, including hardware construction and electrical wiring, communication configuration and debugging, intelligent transformation of each unit and integrated debugging of control network. On this basis, innovative and developmental training projects of intelligent transformation scheme of manufacturing unit are built. Through multi-level, comprehensive, innovative and developmental training, the students’ foundation is strengthened, and their comprehensive ability, innovative development ability and professional quality are also improved.Key words: intelligent manufacturing; training platform; intelligent transformation; training teaching随着《中国制造2025》战略的推进，加快智能制造技术应用是落实工业化和信息化深度融合、打造制收稿日期: 2019-12-30基金项目: 2016年湖南省职业院校教育教学改革研究项目（ZJGB2016136）；湖南省技术创新引导计划项目（2018ZK4058）作者简介: 许怡赦（1975—），男，湖南汨罗，博士，副教授，副院长，主要从事智能制造类实训教学设备和项目开发。

华为电信术语

3G第三代移动通信技术Third Generation Mobile TelephonyARP地址解析协议Address Resolution ProtocolASCII码美国信息交换标准码America Standard Code for Information Interchange ADSL 2/2Plus非对称数字用户线路Asymmetric Digital Subscriber LineASON智能光网络Automatically Switched Optical NetworkATM异步传输模式Asynchronous Transfer ModeBICC与承载无关的呼叫控制协议Bearer Independent Call Control protocolBTS基站收发台Base transceiver stationCDMA2000码分多址Code Division Multiple AccessCDMA码分多址Code Division Multiple AccessDWDM密集波分复用Dense Wavelength Division MultiplexingDSL数字用户环路Digital Subscriber LoopEDGE增强型分组无线通信业务Enhanced Data rates for GSM EvolutionFMC固定与移动网络的融合Fixed-Mobile ConvergenceFTTH光纤到户Fiber To The HomeGPRS通用分组无线业务General Packet Radio ServiceGSM全球移动通讯系统Global System for Mobile CommunicationsHSDPA高速下行分组接入High-Speed Downlink Packet AccessH.320/H.323 H.320/H.323ITU国际电信联盟ITUISO ISO 国际标准化组织IEEE美国电气及电子工程师学会Institute of Electrical and Electronics Engineers IPTN IP电信网IP Telecom NetworkISDN综合业务数字网Integrated Serviced Digital NetworkIPTV交互式网络电视Internet Protocol TelevisionIP互联网络协议Internet ProtocolIN智能网络Intelligent NetworkIMS IP多媒体子系统IP Multimedia SubsystemIAD综合接入设备Integrated Access DeviceLAN局域网Local Area NetworkMPLS多协议标记交换Multiprotocol Label SwitchingMAN城域网Metropolitan Area NetworkMSTP多业务传送平台Multi-service Transport PlatformMSAN多业务网络接入Multi-Service Access NetworkMMS多媒体信息服务Multimedia Message ServiceMBMS组播和广播业务Multimedia Broadcast / Multicast ServiceNGN下一代网络Next-Generation NetworkONT光网络终端Optical Network TerminalPSTN公共交换电话网Public Switched Telephone NetworkRouter路由器RouterSIP会话初始协议Session Initiation ProtocolSoftswitch软交换SoftswitchSONET同步光缆网Synchronous optical networkSMS短消息服务Short Message ServiceSDH同步数字系列Synchronous digital hierarchyTriple Play语音、数据和视频业务融合Triple PlayUMTS通用移动通信系统Universal Mobile Telecommunications System VoIP语音IP电话Voice over Internet ProtocolVPN虚拟专用网Virtual private networkVLAN虚拟局域网Virtual Local Area NetworkVDSL2高速数字用户环路Very-high-bit-rate Digital Subscriber loopWAP WAP无线应用协议Wireless ApplicationProtocolWiMAX微波存取全球互通Worldwide Interoperability for Microwave Access WLAN无线局域网络Wireless Local Area NetworksWCDMA宽带码分多址Wideband Code Division Multiple Access包网络packet networks包交换网络packet switching networks分组网络PBN Packet Based Networks分组数据网络packet data networks互联网服务提供商internet service providerCarrier service provider：载波服务供应商network operator 包含电信运营商的网络运营商netwok provider网络提供商PAS- personal access system 个人接入系统1. PHS小灵通2. flop phone 翻盖手机3. slide phone 滑盖手机4. bar phone 直板手机5. 中国电信China Telecom6. 中国联通China Uni-com7. 中国铁通China Rail-com8. 中国移动China Mobile9. Nokia 诺基亚(from Finland)10. Motolora 摩托罗拉(from the United States)11. L.G. (from South Korea)12. Samsung 三星(from South Korea)13. CECT 中电通信。

FushionServer XH321服务器节点技术白皮书

华为FusionServer Pro XH321 服务器节点技术白皮书技术白皮书前言前言概述本文档详细介绍XH321 V5的外观特点、性能参数以及部件兼容性等内容，让用户对XH321 V5有一个深入细致的了解。

读者对象本文档主要适用于以下工程师：l 华为售前工程师l 渠道伙伴售前工程师l 企业售前工程师符号约定在本文中可能出现下列标志，它们所代表的含义如下。

技术白皮书目录目录前言 (ii)1产品概述 (1)2产品特点 (2)3物理结构 (5)4逻辑结构 (7)5硬件描述 (8)5.1前面板 (8)5.1.1外观 (8)5.1.2指示灯和按钮 (9)5.1.3 接口 (10)5.1.4 安装位置 (12)5.2 处理器 (13)5.3 内存 (13)5.3.1内存标识 (13)5.3.2内存子系统体系结构 (15)5.3.3内存兼容性信息 (15)5.3.4内存安装准则 (17)5.3.5内存插槽位置 (17)5.3.6内存保护技术 (19)5.4 存储 (19)5.4.1硬盘配置 (19)5.4.2硬盘编号 (20)5.4.3RAID 控制卡 (20)5.5IO 扩展 (21)5.5.1PCIe 卡 (21)5.5.2PCIe 插槽 (21)5.5.3PCIe 插槽说明 (21)5.6 单板 (22)5.6.1 主板 (22)6产品规格 (24)华为FusionServer Pro XH321 V5 服务器节点技术白皮书目录6.1技术规格 (24)6.2环境规格 (27)6.3物理规格 (28)7软硬件兼容性 (29)8管制信息 (30)8.1 安全 (30)8.2 维保与保修 (33)9系统管理 (34)10通过的认证 (36)A 附录 (38)A.1术语 (38)A.2缩略语 (39)A.3产品序列号 (44)A.4工作温度规格限制 (45)A.4.1温度规格限制（配置RAID 控制卡超级电容） (45)A.4.2温度规格限制（配置光模块） (46)A.4.3温度规格限制（配置Avago SAS3004iMR RAID 控制标卡+M.2 FRU） (48)A.4.4温度规格限制（配置不同型号处理器） (50)技术白皮书 1 产品概述1产品概述XH321 V5服务器节点（以下简称XH321 V5）是华为X6000服务器的节点，X6000服务器机箱为2U，可以安装4个XH321 V5。

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Exception Path Tx From Plugins
Worker1
Worker0
Worker2
Worker3
Port0
15 | © 2013 Wind River. All Rights Reserved.
Port1
- Wind River Confidential -
Worker4
Fast-path Framework
Interrupt-free Timer-free Polling-based
12
| © 2013 Wind River. All Rights Reserved.
- Wind River Confidential -
Fast-path Architecture (cont.)
Asynchronous Fast Fath-path plugin (separate process)
Content Inspection Engine (CIE) Flow Analysis Engine (FAE)
Summary
2
| © 2013 Wind River. All Rights Reserved.
- Wind River Confidential -
Converged Strategies:
- Wind River Confidential -
Fast-path Architecture - Worker
The Fast-path Worker is an entity that polls a single queue, and performs actions over the packets in this queue An Fast-path worker is NOT a separate thread Every queue belongs to a certain traffic-source; a traffic-source could be:
Worker2
Worker0
Core0
Core1
Fast-path Framework
One core can drive several Fast-path workers (or even all of them)
13
| © 2013 Wind River. All Rights Reserved.
CoreCore来自CoreCore
Core
Core
Core
Core
6
| © 2013 Wind River. All Rights Reserved.
- Wind River Confidential -
Increased modularity
FAE CIE FAE CIE Stack + Fast-path
– assigned cores are specified through configuration
Extensibility – 3rd-party apps/plug-ins communicate with the Fast-path via:
1. A call-back (called from fast-path context) 2. Or through a queue (messages intercepted in a separate thread/process) – Other processes can share cores with the Fast-path
– User-space Drivers
PMD, no IRQs ->faster context switching Receives in bursts
– Huge Page Tables
Reduced application latency
Fewer cache misses
User-Mode Drivers User IO Module User-Space Kernel-Space
14 | © 2013 Wind River. All Rights Reserved.
- Wind River Confidential -
Fast-path Architecture – Worker (cont.)
Fast -path workers feed the Fast -path framework with packets from the various traffic-sources (one or more worker per traffic-source)
– The Rx side of a physical port – Packets coming from plugins – OS exception-path – etc.
A traffic-source may employ several queues – each queue is handled by a different Fast-path worker
Fast-path in NFV
9
| © 2013 Wind River. All Rights Reserved.
- Wind River Confidential -
Fast-path Introduction
Next Generation Fast-path for INP Consists of:
V A L U E
INP 3.4
Stack + Fast-path
Stack + Fast-path
Stack + Fast-path
Modularity
NGFW
INP 3.5
FAE Stack Fast-path Fast-path CIE Fast-path Stack
FAE
CIE
Stack Fast-path
11
| © 2013 Wind River. All Rights Reserved.
- Wind River Confidential -
Fast-path Architecture (cont.)
Fast-path framework – a reactive system
– It polls queues, and reacts to incoming messages
Management Plane
Linux
(w/DPDK)
Application 1 Acceleration Engine
Core Affinity
Content Inspection Engine
Core Affinity
2
Flow Analysis Engine
Core Affinity
3
Linux User Space Linux Kernel Space
Fast-path Architecture – Worker (cont.)
All the Fast-path workers that handle queues from the same traffic-source are termed an Fast-path Cluster The Fast-path main-loop runs over a list of Fast-path workers (not necessarily from the same cluster), allowing each worker to poll its own queue; Consequently, a single core is able to accommodate multiple Fast-path workers and handle multiple traffic-sources (specifically, multiple physical ports)
5
| © 2013 Wind River. All Rights Reserved.
- Wind River Confidential -
Intelligent Network Platform
Native Linux Apps
Customer Data Plane App Customer Customer Customer Data Plane App Customer Data Plane App Data Plane App Data Plane App
The do-it-yourself inflection point
INP
NFV
Level of Effort
The Elevator The Stairs
QoS
Security DPI Workload Consolidation
Inflection Point
Increasing Functionality
Leads to Innovation, Acceleration and Optimized Integration
3
| © 2013 Wind River. All Rights Reserved.
- Wind River Confidential -
Intel® DPDK Components
– Scales better than Linux native – Pre-integrated w/WRLinux
port0 port1
…
portN
– Easy to use/debug
4 | © 2013 Wind River. All Rights Reserved.

11_Intelligent_Network_Platform

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华为电信术语

FushionServer XH321服务器节点技术白皮书