28 A Feature-level Fusion of Appearance and Passive Depth Information for Face Recognition

合集下载

决策层特征融合decisionlevelidentityfusion

PPT文档演模板
决策层特征融合 decisionlevelidentityfusion
Introduction
Decision-level fusion
Seeks to process identity declarations from multiple sensors to achieve a joint declaration of identity.
PPT文档演模板
决策层特征融合 decisionlevelidentityfunce
PPT文档演模板
Main technique – hypothesis testing
Define two hypothesis 1. A null hypothesis, H0 (原假设) 2. An alternative hypothesis,H1 (备择假设)
决策层特征融合 decisionlevelidentityfusion
Classical inference
Main technique – hypothesis testing
Two assumptions are required
PPT文档演模板
决策层特征融合 decisionlevelidentityfusion
Introduction
Decision-Level Fusion Techniques
Classical inference Bayesian inference Dempster-Shafer’s method Generalized evidence processing theory Heuristic methods
here
n trials, occurrence of k times

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。

人脸识别介绍_IntroFaceDetectRecognition

Knowledge-based Methods: Summary
Pros:
Easy to come up with simple rules Based on the coded rules, facial features in an input image are extracted first, and face candidates are identified Work well for face localization in uncluttered background
Template-Based Methods: Summary
Pros:
Simple
Cons:
Templates needs to be initialized near the face images Difficult to enumerate templates for different poses (similar to knowledgebased methods)
Knowledge-Based Methods
Top Top-down approach: Represent a face using a set of human-coded rules Example:
The center part of face has uniform intensity values The difference between the average intensity values of the center part and the upper part is significant A face often appears with two eyes that are symmetric to each other, a nose and a mouth

多模态数据融合英语

多模态数据融合英语Multimodal Data Fusion: Bridging the Gap between Diverse Information Sources.In the era of big data, the amount of information available to us is growing exponentially. This information often comes in various forms, such as text, audio, video, and images, each carrying its unique set of features and contextual information. To effectively extract meaningful insights from this diverse range of data, multimodal data fusion has become a crucial technique.Multimodal data fusion, simply put, is the process of combining and integrating information from multiple modalities or sources to create a comprehensive representation. It allows us to leverage the complementary nature of different data types, enhancing our understanding and analysis capabilities.Importance of Multimodal Data Fusion.The importance of multimodal data fusion lies in its ability to overcome the limitations of single-modality data. For instance, text data may provide detailed descriptive information, but it lacks visual cues or emotional context. On the other hand, audio and video data can capture non-verbal cues and emotional expressions that are often lostin textual representations. By combining these modalities, we can gain a deeper understanding of the underlying phenomena.Multimodal data fusion is also crucial in scenarios where data from different sources is incomplete or noisy.By combining multiple modalities, we can often compensatefor the missing or unreliable information in one modality with the help of another. This fusion of information notonly improves the quality of data but also enhances the reliability of the derived insights.Techniques of Multimodal Data Fusion.There are several techniques used for multimodal datafusion, each with its own strengths and applications. Someof the commonly used techniques include:1. Feature-level Fusion: This approach involves combining the features extracted from different modalitiesat an early stage. It allows for the integration of complementary information from various sources, but it can be challenging to handle the different types of featuresand their associated semantic gaps.2. Decision-level Fusion: In this technique, decisionsor predictions made by individual modalities are combinedto form a final decision. This approach is often used in scenarios where the modalities are highly diverse or whenit's desirable to maintain the independence of individual modalities.3. Model-level Fusion: Here, multiple models trained on different modalities are combined to create a unified model. This approach leverages the strengths of each model, enabling it to capture a broader range of information. However, it can be computationally expensive and requirescareful consideration of model complexity andgeneralization capabilities.Applications of Multimodal Data Fusion.Multimodal data fusion finds applications in various domains, including:1. Human-Computer Interaction (HCI): In HCI, multimodal data fusion enables computers to understand and respond to a wide range of user inputs, including voice, gesture, and facial expressions. This integration of multiple input modalities improves the naturalness and efficiency of human-computer interactions.2. Multimedia Processing: In the field of multimedia processing, multimodal data fusion is used to analyze and understand complex multimedia content, such as movies, TV shows, and advertisements. By combining audio, video, and textual information, we can gain insights into the emotional content, narrative structure, and semantic meaning of these multimedia pieces.3. Sentiment Analysis: Sentiment analysis aims to determine the emotional sentiment behind textual or spoken content. By combining textual data with audio and video modalities, such as facial expressions and tone of voice, we can more accurately capture the emotional context and sentiment behind the communication.Challenges and Future Directions.Despite its promise and widespread applications, multimodal data fusion faces several challenges. One of the key challenges is dealing with the semantic gap, which arises due to the inherent differences in the representations and interpretations of information across different modalities. Addressing this gap requires sophisticated fusion techniques that can effectively bridge the semantic differences.Another challenge lies in handling the complexity and diversity of real-world data. In many scenarios, the available data may be noisy, incomplete, or inconsistent,making it difficult to extract meaningful insights. Future research needs to focus on developing robust fusion methods that can handle such challenges and extract reliable information from diverse data sources.Moreover, with the increasing volume and velocity of data, efficient and scalable fusion techniques are needed. Current fusion methods may not be able to handle the large-scale data efficiently, necessitating the development of new algorithms and frameworks that can handle the computational demands of multimodal data fusion.In conclusion, multimodal data fusion represents a powerful tool for整合不同来源的信息，提升我们对复杂现象的理解和分析能力。

add融合特征

融合特征（Fusion Features）通常用于将多个不同类型或来源的特征信息结合起来，以获得更全面、准确的表示。

在机器学习和计算机视觉等领域中，融合特征可以提供更丰富的信息，从而改善模型的性能。

以下是一些常见的融合特征方法：
1. 特征级融合（Feature-level Fusion）：将不同类型的特征进行组合或连接，形成一个更综合的特征向量。

例如，将图像的颜色特征和纹理特征进行拼接，以获取更全面的视觉表示。

2. 决策级融合（Decision-level Fusion）：基于多个独立模型或分类器的输出，通过投票、加权平均等方式进行决策的融合。

例如，在人脸识别任务中，通过多个人脸检测器的结果进行投票决策，提高识别准确度。

3. 分层级融合（Hierarchical Fusion）：将多个特征层次化地进行融合，以逐步提取和整合信息。

例如，在图像识别任务中，可以通过级联的卷积神经网络（CNN）结构，将低级特征和高级特征逐渐融合，提高分类性能。

4. 基于注意力机制的融合（Attention-based Fusion）：通过学习权重或注意力分配，将不同特征的重要性进行动态调整。

例如，在自然语言处理中，通过注意力机制可以根据输入序列的不同部分，自适应地
聚焦于关键信息，提高模型对输入的建模能力。

以上是一些常见的融合特征方法，它们可以用于将多个特征源融合到一起，以改善模型的性能和表达能力。

具体选择哪种融合方法，需要根据任务需求和特征类型进行综合考虑。

基于多特征融合与ResNet的海面溢油区识别研究

Research on Identification of Sea Surface Oil Spill Zone Based on Multi- feature Fusion and Residual Network ZHANG Xiaoxiao1, NIU Fu2, MAO Jianping1, AN Jubai1, GUO Hao1
随着海洋石油资源开发的不断增加，溢油事故发生较为频繁，对海洋和沿海地区造成了严重的环境和经济影响，为了防止溢油灾害，必须检测溢油的位置。SAR 以其广域、全天候的监视能力，被认为是最适合漏油监测的传感器之一。 [1] 然而在全极化 SAR 图像中经常出现“类油膜”（如生物油膜、低风速区、乳化油膜、大气重力波等）现象，在全极化 SAR 图像上类油膜与油膜均呈现为暗色区域，两者的后向散射系数和灰度值很相似，在识别时易对两者产生混淆，对利用 SAR 图像进行海面溢油区检测产生了严重影响。所以对全极化 SAR 图像上的类油膜和油膜现象进行高效的分类对降低海面溢油监测的虚警率尤为重要。
Computer Engineering and Applications 计算机工程与应用
2021，57（14） 267
基于多特征融合与 ResNet 的海面溢油区识别研究
张晓晓 1，牛福 2，毛健平 1，安居白 1，郭浩 1 1. 大连海事大学信息科学技术学院，辽宁大连 116026 2. 山东交通学院汽车工程学院，济南 250357
268 2021，57（14）
Computer Engineering and Applications 计算机工程与应用
overfitting and obtain more reliable experimental results, K -cross validation and ROC curve experiments are also conducted in this paper. The results show that the algorithm proposed in this paper is effective. Key words：full polarimetric SAR images; multi-feature fusion; oil film; oil-like film; deep residual network

特征级融合的概念

特征级融合1. 概念定义特征级融合（Feature-level fusion）是指将来自不同特征提取方法或特征表示的多个特征进行融合，生成一个更具有代表性和丰富性的特征向量。

特征级融合是多模态数据融合的一种常用方法，通过将不同模态的特征进行融合，可以提取出更全面、准确和鲁棒的特征表示，从而提高模型的性能。

特征级融合可以分为低层次特征融合和高层次特征融合。

低层次特征融合主要是将不同特征提取方法得到的底层特征进行融合，例如将图像的颜色特征、纹理特征和形状特征进行融合。

高层次特征融合则是在低层次特征的基础上，将不同模态的特征进行融合，例如将图像特征和文本特征进行融合。

2. 重要性特征级融合在多模态数据处理中具有重要的作用，具体体现在以下几个方面：2.1 提高特征的多样性和丰富性通过特征级融合，可以将来自不同特征提取方法或特征表示的多个特征进行融合，从而生成一个更具有多样性和丰富性的特征向量。

这样可以更全面地描述数据的特征，提高模型的表达能力和泛化能力。

2.2 弥补单一特征的不足不同特征提取方法或特征表示往往具有不同的优势和局限性。

通过特征级融合，可以将不同特征的优势进行整合，弥补单一特征的不足。

例如，在图像识别任务中，可以将颜色特征、纹理特征和形状特征进行融合，以提高图像识别的准确率。

2.3 提高模型的鲁棒性通过特征级融合，可以从不同角度对数据进行建模，从而提高模型的鲁棒性。

当某个特征在某些情况下不可靠或不可用时，其他特征仍然可以提供有用的信息，从而保证模型的稳定性和可靠性。

2.4 减少特征维度特征级融合可以将多个特征融合成一个更具有代表性的特征向量，从而减少特征的维度。

这样可以降低计算复杂度和存储开销，并提高模型的训练和推理效率。

3. 应用特征级融合在各个领域都有广泛的应用，以下列举几个典型的应用场景：3.1 多模态情感分析多模态情感分析是指通过分析多种模态（如图像、文本、语音等）数据中的情感信息，来推断数据的情感状态。

小学上册第十二次英语第2单元自测题

小学上册英语第2单元自测题英语试题一、综合题(本题有100小题，每小题1分，共100分.每小题不选、错误，均不给分)1.The meerkat stands guard for its ______ (家族).2.The scientist conducts _____ (实验) in the lab.3.The parrot has bright ______ (羽毛).4.What do you call the sound a dog makes?A. MeowB. BarkC. RoarD. Whistle5.The _____ (开花) season brings joy to many.6.Certain plants can ______ (调节) local climates.7.My favorite animal is a ________ (狗) because it is very friendly.8.I watched a _______ (小鹦鹉) mimic sounds.9. A pendulum swings back and ______ (forth).10.We have a garden with many _______ (我们有一个花园，里面有很多_______).11.The _____ (植物故事讲述) can connect people to their heritage.12.What is 8 + 6?A. 12B. 14C. 16D. 18B 1413. civilization is known for its advanced ________ (天文学). The Maya14. A chemical reaction can be driven by energy from the ______.15.I enjoy drawing and painting in my free time.16.Understanding how to attract beneficial ______ can help your garden thrive. (了解如何吸引有益生物可以帮助你的花园茁壮成长。

数据融合英语

数据融合英语Data FusionData fusion is the process of combining data from multiple sources to produce more accurate, reliable, and comprehensive information. It involves the integration of data from different sensors, platforms, and modalities to provide a more complete picture of a given situation or phenomenon. The goal of data fusion is to extract meaningful information from diverse and often conflicting data sources, and to present this information in a form that is useful for decision-making.Data fusion can be categorized into three types: sensor-level fusion, feature-level fusion, and decision-level fusion. Sensor-level fusion involves the integration of raw sensor data from multiple sources to produce a single, unified data stream. Feature-level fusion involves the extraction of relevant features from each data source, and the integration of these features to produce a more complete representation of the underlying phenomenon. Decision-level fusion involves the integration of decision outputs from multiple sources to produce a final decision.Data fusion has applications in a wide range of fields, including military,aerospace, transportation, environmental monitoring, and healthcare. In military applications, data fusion is used to integrate information from multiple sensors and platforms to provide situational awareness and support decision-making. In aerospace applications, data fusion is used to integrate data from multiple sensors to improve navigation and control. In transportation applications, data fusion is used to integrate data from multiple sources to improve traffic management and safety. In environmental monitoring, data fusion is used to integrate data from multiple sensors to provide early warning of natural disasters. In healthcare, data fusion is used to integrate data from multiple sources to support diagnosis and treatment.Data fusion is a challenging field that requires expertise in signal processing, statistics, machine learning, and computer science. It involves the development of algorithms and techniques for integrating and analyzing data from multiple sources, as well as the development of software and hardware systems for implementing these algorithms. Despite these challenges, data fusion has the potential to revolutionize the way we collect, analyze, and use data to make decisions in a wide range of applications.。

多层次特征融合低照度图像增强算法

ｄｏｉ：１０．３９６９／ｊ．ｉｓｓｎ．１００３－３１０６．２０２３．０４．０２５引用格式：梁礼明，朱晨锟，何安军．多层次特征融合低照度图像增强算法［Ｊ］．无线电工程，２０２３，５３（４）：９４６－９５６．［ＬＩＡＮＧＬｉｍｉｎｇ，ＺＨＵＣｈｅｎｋｕｎ，ＨＥＡｎｊｕｎ．ＭｕｌｔｉｌｅｖｅｌＦｅａｔｕｒｅＦｕｓｉｏｎＡｌｇｏｒｉｔｈｍｆｏｒＬｏｗＩｌｌｕｍｉｎａｔｉｏｎＩｍａｇｅＥｎｈａｎｃｅｍｅｎｔ［Ｊ］．ＲａｄｉｏＥｎｇｉｎｅｅｒｉｎｇ，２０２３，５３（４）：９４６－９５６．］多层次特征融合低照度图像增强算法梁礼明，朱晨锟，何安军（江西理工大学电气工程与自动化学院，江西赣州３４１４１１）摘　要：针对成像设备在夜间等低照度环境下采集的图像存在细节丢失、动态范围较窄和大量噪声等特点，导致采集图像清晰度低、可用性不高和识别性较差等问题，提出了一种多层次特征融合（ＭｕｌｔｉｌｅｖｅｌＦｅａｔｕｒｅＦｕｓｉｏｎ，ＭＦＦＮｅｔ）算法。

该算法利用多尺度采样构建Ｕ型网络，并引入多种注意力机制多线程处理图像流，各支路特征向量跨通道交互，协同渐进式抑制冗余信息。

高效运用特征融合模块强化对低尺度纹理细节和多层次特征的感知。

设计了由峰值信噪比（ＰｅａｋＳｉｇｎａｌｔｏＮｏｉｓｅＲａｔｉｏ，ＰＳＮＲ）和结构相似性（ＳｔｒｕｃｔｕｒａｌＳｉｍｉｌａｒｉｔｙ，ＳＳＩＭ）指标构成的损失函数，有目的地引导网络由浅到深地学习图像之间的映射关系，从而加快模型收敛速度，助力提高模型性能和图像增强。

所提算法在ＬＯＬ数据集ＬｏｗＬｉｇｈｔＤａｔａｓｅｔ上进行了相关实验和测试。

其ＰＳＮＲ、ＳＳＩＭ和学习感知图像块相似度（ＬｅａｒｎｅｄＰｅｒｃｅｐｔｕａｌＩｍａｇｅＰａｔｃｈＳｉｍｉｌａｒｉｔｙ，ＬＰＩＰＳ）等６种客观评价指标上整体优于大部分先进算法。

实验结果表明，所构建的模型能有效抑制图像失真、噪声问题并显著提高图像质量和照度。

关键词：多尺度特征融合；低照度图像；Ｕ型网络；注意力机制；图像增强中图分类号：ＴＰ３９１．４１文献标志码：Ａ开放科学（资源服务）标识码（ＯＳＩＤ）：文章编号：１００３－３１０６（２０２３）０４－９４６－１１ＭｕｌｔｉｌｅｖｅｌＦｅａｔｕｒｅＦｕｓｉｏｎＡｌｇｏｒｉｔｈｍｆｏｒＬｏｗＩｌｌｕｍｉｎａｔｉｏｎＩｍａｇｅＥｎｈａｎｃｅｍｅｎｔＬＩＡＮＧＬｉｍｉｎｇ，ＺＨＵＣｈｅｎｋｕｎ，ＨＥＡｎｊｕｎ（ＳｃｈｏｏｌｏｆＥｌｅｃｔｒｉｃａｌＥｎｇｉｎｅｅｒｉｎｇａｎｄＡｕｔｏｍａｔｉｏｎ，ＪｉａｎｇｘｉＵｎｉｖｅｒｓｉｔｙｏｆＳｃｉｅｎｃｅａｎｄＴｅｃｈｎｏｌｏｇｙ，Ｇａｎｚｈｏｕ３４１４１１，Ｃｈｉｎａ）Ａｂｓｔｒａｃｔ：Ｉｎｏｒｄｅｒｔｏｓｏｌｖｅｔｈｅｐｒｏｂｌｅｍｓｏｆｌｏｗｒｅｓｏｌｕｔｉｏｎ，ｌｏｗａｖａｉｌａｂｉｌｉｔｙａｎｄｐｏｏｒｒｅｃｏｇｎｉｔｉｏｎｃａｕｓｅｄｂｙｔｈｅｌｏｓｓｏｆｄｅｔａｉｌ，ｎａｒｒｏｗｄｙｎａｍｉｃｒａｎｇｅａｎｄｌａｒｇｅａｍｏｕｎｔｏｆｎｏｉｓｅｉｎｔｈｅｉｍａｇｅｓｃｏｌｌｅｃｔｅｄｂｙｉｍａｇｉｎｇｅｑｕｉｐｍｅｎｔｉｎｌｏｗｉｌｌｕｍｉｎａｔｉｏｎｅｎｖｉｒｏｎｍｅｎｔｓｕｃｈａｓｎｉｇｈｔ，ａＭｕｌｔｉｌｅｖｅｌＦｅａｔｕｒｅＦｕｓｉｏｎ（ＭＦＦＮｅｔ）ａｌｇｏｒｉｔｈｍｉｓｐｒｏｐｏｓｅｄ．Ｉｎｔｈｉｓａｌｇｏｒｉｔｈｍ，ｍｕｌｔｉｓｃａｌｅｓａｍｐｌｉｎｇｉｓｆｉｒｓｔｌｙｕｓｅｄｔｏｃｏｎｓｔｒｕｃｔａＵｓｈａｐｅｄｎｅｔｗｏｒｋ，ａｎｄｍｕｌｔｉｐｌｅａｔｔｅｎｔｉｏｎｍｅｃｈａｎｉｓｍｓａｒｅｉｎｔｒｏｄｕｃｅｄｔｏｐｒｏｃｅｓｓｔｈｅｉｍａｇｅｓｔｒｅａｍｉｎｍｕｌｔｉｐｌｅｔｈｒｅａｄｓ．Ｆｅａｔｕｒｅｖｅｃｔｏｒｓｏｆｅａｃｈｂｒａｎｃｈｉｎｔｅｒａｃｔｗｉｔｈｅａｃｈｏｔｈｅｒａｃｒｏｓｓｃｈａｎｎｅｌｓ，ａｎｄｃｏｏｐｅｒａｔｅｔｏｓｕｐｐｒｅｓｓｒｅｄｕｎｄａｎｔｉｎｆｏｒｍａｔｉｏｎｐｒｏｇｒｅｓｓｉｖｅｌｙ．Ｓｅｃｏｎｄｌｙ，ｆｅａｔｕｒｅｆｕｓｉｏｎｍｏｄｕｌｅｓａｒｅｅｆｆｉｃｉｅｎｔｌｙｕｓｅｄｔｏｅｎｈａｎｃｅｔｈｅｐｅｒｃｅｐｔｉｏｎｏｆｌｏｗｓｃａｌｅｔｅｘｔｕｒｅｄｅｔａｉｌｓａｎｄｍｕｌｔｉｌｅｖｅｌｆｅａｔｕｒｅｓ．Ｆｉｎａｌｌｙ，ａｌｏｓｓｆｕｎｃｔｉｏｎｃｏｍｐｏｓｅｄｏｆＰｅａｋＳｉｇｎａｌｔｏＮｏｉｓｅＲａｔｉｏ（ＰＳＮＲ）ａｎｄＳｔｒｕｃｔｕｒａｌＳｉｍｉｌａｒｉｔｙ（ＳＳＩＭ）ｉｎｄｅｘｉｓｄｅｓｉｇｎｅｄｔｏｐｕｒｐｏｓｅｆｕｌｌｙｇｕｉｄｅｔｈｅｎｅｔｗｏｒｋｔｏｌｅａｒｎｔｈｅｍａｐｐｉｎｇｒｅｌａｔｉｏｎｓｈｉｐｂｅｔｗｅｅｎｉｍａｇｅｓｆｒｏｍｓｈａｌｌｏｗｔｏｄｅｅｐ，ｓｏａｓｔｏａｃｃｅｌｅｒａｔｅｔｈｅｍｏｄｅｌｃｏｎｖｅｒｇｅｎｃｅｓｐｅｅｄａｎｄｈｅｌｐｍｏｄｅｌｐｅｒｆｏｒｍａｎｃｅｉｍｐｒｏｖｅｍｅｎｔａｎｄｉｍａｇｅｅｎｈａｎｃｅｍｅｎｔ．ＴｈｅｐｒｏｐｏｓｅｄａｌｇｏｒｉｔｈｍｉｓｔｅｓｔｅｄｏｎＬＯＬ（ＬｏｗＬｉｇｈｔ）ｄａｔａｓｅｔ．ＴｈｅｓｉｘｏｂｊｅｃｔｉｖｅｅｖａｌｕａｔｉｏｎｉｎｄｅｘｅｓｏｆＰＳＮＲ，ＳＳＩＭａｎｄＬｅａｒｎｅｄＰｅｒｃｅｐｔｕａｌＩｍａｇｅＰａｔｃｈＳｉｍｉｌａｒｉｔｙ（ＬＰＩＰＳ）ａｒｅｂｅｔｔｅｒｔｈａｎｔｈｏｓｅｏｆｍｏｓｔａｄｖａｎｃｅｄａｌｇｏｒｉｔｈｍｓ．Ｅｘｐｅｒｉｍｅｎｔａｌｒｅｓｕｌｔｓｓｈｏｗｔｈａｔｔｈｅｐｒｏｐｏｓｅｄｍｏｄｅｌｃａｎｅｆｆｅｃｔｉｖｅｌｙｓｕｐｐｒｅｓｓｉｍａｇｅｄｉｓｔｏｒｔｉｏｎａｎｄｎｏｉｓｅ，ａｎｄｓｉｇｎｉｆｉｃａｎｔｌｙｉｍｐｒｏｖｅｉｍａｇｅｑｕａｌｉｔｙａｎｄｉｌｌｕｍｉｎａｔｉｏｎ．Ｋｅｙｗｏｒｄｓ：ｍｕｌｔｉｓｃａｌｅｆｅａｔｕｒｅｆｕｓｉｏｎ；ｌｏｗｉｌｌｕｍｉｎａｔｉｏｎｉｍａｇｅ；Ｕｎｅｔ；ａｔｔｅｎｔｉｏｎｍｅｃｈａｎｉｓｍ；ｉｍａｇｅｅｎｈａｎｃｅｍｅｎｔ收稿日期：２０２２－１１－０２基金项目：国家自然科学基金（５１３６５０１７，６１４６３０１８）；江西省自然科学基金面上项目（２０１９２ＢＡＢ２０５０８４）；江西省教育厅科学技术研究重点项目（ＧＪＪ１７０４９１）ＦｏｕｎｄａｔｉｏｎＩｔｅｍ：ＮａｔｉｏｎａｌＮａｔｕｒａｌＳｃｉｅｎｃｅＦｏｕｎｄａｔｉｏｎｏｆＣｈｉｎａ（５１３６５０１７，６１４６３０１８）；ＧｅｎｅｒａｌＰｒｏｇｒａｍｏｆＪｉａｎｇｘｉＰｒｏｖｉｎｃｉａｌＮａｔｕｒａｌＳｃｉｅｎｃｅＦｏｕｎｄａｔｉｏｎｏｆＣｈｉｎａ（２０１９２ＢＡＢ２０５０８４）；ＫｅｙＰｒｏｊｅｃｔｏｆＪｉａｎｇｘｉＰｒｏｖｉｎｃｉａｌＤｅｐａｒｔｍｅｎｔｏｆＥｄｕｃａｔｉｏｎＳｃｉｅｎｃｅａｎｄＴｅｃｈｎｏｌｏｇｙＲｅｓｅａｒｃｈ（ＧＪＪ１７０４９１）工程与应用０　引言在雨天、夜晚等恶劣照明环境下采集的图像称为低照度图像，此类图像难以辨别、缺乏可用性，同时也会对语义分割、图像识别和目标检测等高层任务带来困难。

英语作文描写大象外貌

英语作文描写大象外貌The elephant, a majestic creature of the wild, is an awe-inspiring sight. Its immense size is the first thing that captures one's attention. Towering over the landscape, an adult elephant can reach up to 10 feet in height and weigh several tons. Its skin is thick and wrinkled, resembling the texture of tree bark, with a color that varies from a light gray to a darker, almost slate hue. These wrinkles are not just a feature of its appearance but also serve a practical purpose, helping to regulate its body temperature by trapping moisture.The elephant's head is large and rounded, adorned with two large, floppy ears that are essential for cooling its massive body. These ears are highly sensitive and can detect even the faintest sounds. Its eyes are small and gentle, often surrounded by a fringe of long, dark lashes, which give it an almost tender expression.One of the most distinctive features of the elephant is its trunk, a versatile and powerful appendage. The trunk is a fusion of the nose and upper lip, capable of incredible dexterity. It is used for a multitude of tasks, from picking up food to spraying water for cooling and even as a tool for social interaction. The trunk, with its tens of thousands of muscles, is a testament to the elephant's adaptability and intelligence.The elephant's tusks, when present, are long and curved, protruding from its upper jaw. These are not just for show but are used for digging, stripping bark from trees, and as a formidable weapon when necessary. The tusks are also indicative of the elephant's age and health, growing continuously throughout its life.The body of the elephant is supported by four sturdy legs, ending in large, padded feet that are designed for traversing the diverse terrains of its natural habitat. Its tail is short and tufted, often used for swatting away insects.In summary, the elephant's appearance is a blend of strength and grace, a testament to the evolutionary process that has shaped this extraordinary animal. Its physical attributes are not just for show but are essential tools for survival in the wild, reflecting the adaptability and resilience of this remarkable species.。

介绍人外貌的英语作文

介绍人外貌的英语作文Describing the Physical Appearance of a PersonPhysical appearance is one of the first things we notice about a person when we meet them. It can give us initial impressions and insights into their personality, lifestyle, and background. Describing someone's physical appearance in detail can help paint a vivid picture and allow others to better visualize the individual.When describing a person's physical appearance, it's important to start from the top and work your way down. One of the most noticeable features is the face. The face is the central focus and often what we're immediately drawn to when looking at someone. Describing the shape of the face, such as round, oval, or angular, can provide useful information. The complexion, whether it's fair, olive, or dark, is another important facial characteristic to note. Skin tone and clarity can reveal clues about a person's ethnic heritage and health.Moving down, the eyes are a captivating feature that deserve close attention. Eye color, whether blue, green, brown, or hazel, is adistinct trait. The size and shape of the eyes, whether they're small and almond-shaped or large and wide-set, can convey different qualities. Eyebrows are another facial element that's worth describing. Are they thick and bold or thin and arched? The way a person's eyebrows are groomed and styled can suggest their personal style and grooming habits.The nose is a central facial feature that's worth noting as well. Is it small and pert or large and pronounced? Does it have a straight bridge or is it slightly curved? A person's nose can provide insight into their ethnic background. The mouth is the final major facial feature to describe. Are the lips full and pouty or thin and tight-lipped? The shape and size of the mouth can reveal a lot about a person's personality, from sensual and expressive to reserved and serious.Moving below the face, the neck is an often overlooked but important part of physical appearance. Is it long and slender or short and thick? Does it have any distinguishing marks or features like a prominent Adam's apple? Broadening out, the shoulders are the next area to consider. Are they narrow and slight or broad and muscular? The overall build and frame of a person's upper body can give clues about their physical fitness and activity level.The torso is another key area to describe. Is the person's midsectionslim and trim or thick and sturdy? Do they have a flat, toned stomach or a rounder, softer belly? The overall proportions of the upper body in relation to the lower body can indicate a person's body type, such as an hourglass, pear, or apple shape.Continuing down, the arms and hands are worth noting as well. Are the arms thin and lean or thick and muscular? Do the hands appear small and delicate or large and calloused? Any distinguishing features like tattoos, scars, or jewelry on the hands and arms can provide additional details. The legs are the final major area to describe when painting a full picture of someone's physical appearance. Are they long and slender or short and thick? Do they appear toned and fit or soft and shapeless?In addition to the various body parts, clothing and accessories can also be important elements to include when describing physical appearance. What is the person wearing? Is their style formal and buttoned-up or casual and laid-back? Do they have any distinguishing accessories like glasses, hats, or jewelry that stand out? All of these details can contribute to the overall visual impression.Ultimately, describing physical appearance is about providing a comprehensive and vivid picture of what someone looks like. By carefully observing and detailing all the different features from head to toe, you can create a detailed portrait that allows others tovisualize the individual. Physical appearance is just one aspect of a person, but it can provide valuable insights and first impressions. With a keen eye for detail, you can bring a person's look to life through descriptive language.。

介绍将表演的节目英语作文好句

介绍将表演的节目英语作文好句1. The performance includes a variety of acts such as dance, music, and comedy.2. The show features talented performers showcasing their skills and entertaining the audience.3. There will be a total of 10 performances throughout the evening.4. Each act will bring a unique and captivating element to the show.5. The audience can expect a diverse range of entertainment from the performers.6. The performers have been rehearsing tirelessly to ensure a seamless and enjoyable show.7. The show promises to be a dynamic and engaging experience for all attendees.8. The program will also include interactive segments to involve the audience.9. The performance will take place in a state-of-the-art theater with excellent acoustics and lighting.10. The show will highlight the rich cultural traditions of the region through various performances.11. The audience will be treated to a fusion of modern and traditional acts.12. The performance will showcase the talents of both established and emerging artists.13. With a total of 15 acts, the audience will be entertained from start to finish.14. The show will feature a mix of solo, group, and ensemble performances.15. The program has been carefully curated to offer something for everyone's tastes.16. The performance will transport the audience to different worlds through the art of storytelling.17. The show will blend elements of drama, music, and dance to create a captivating experience.18. The show will last for approximately three hours, with intermissions for refreshments.19. Each performance will be accompanied by live music to enhance the overall experience.20. The performances are designed to appeal to both young and older audiences.21. There will be a surprise guest appearance by a renowned artist during the show.22. The performance will convey powerful messages through compelling storytelling and visuals.23. With a total of 20 performances, the program offers a rich and varied entertainment experience.24. The audience will have the opportunity to interact with the performers during certain segments of the show.25. The show will incorporate elements of comedy to lighten the mood and entertain the audience.26. The performance will feature a blend of traditional and contemporary art forms.27. The program will captivate the audience with visually stunning and imaginative performances.28. The audience will be enchanted by the graceful movements and emotive expressions of the dancers.29. The show will showcase the vibrant and diversetalents of the local performing arts community.30. The performance will leave a lasting impression on the audience through its powerful and moving content.31. With a total of 25 acts, the show promises to be an unforgettable and exhilarating experience.32. The program will take the audience on a journey through different cultures and artistic expressions.33. The show will offer a delightful blend of high-energy and introspective performances.34. The performance will feature awe-inspiring displays of talent and creativity by the artists.35. The audience will be treated to a sensory feast of visually stunning performances and music.36. The show will leave a lasting impact with itsthought-provoking and emotionally resonant content.37. The performance will demonstrate the beauty and power of artistic expression in all its forms.38. With a total of 30 acts, the show will keep the audience engaged and entertained throughout.39. The program will appeal to a wide range of interests, from music to theater to dance.40. The performance will ignite the imagination and stir the emotions of the audience.41. The show will feature a mix of classical and contemporary performances to cater to diverse tastes.42. The program will offer a balance of lighthearted entertainment and thought-provoking content.43. The audience will be drawn into the world of the performers through immersive storytelling and visuals.44. The performance will showcase the technical precision and artistry of the performers.45. The show will celebrate the rich cultural heritage of the region through its diverse performances.46. The program will provide a platform for emerging talents to showcase their skills and creativity.47. The performance will leave a lasting impression with its powerful and poignant message.48. With a total of 35 acts, the show will offer a non-stop extravaganza of entertainment.49. The program will demonstrate the universal language of art and its ability to connect people.50. The performance will inspire and uplift the audience through its moving and evocative content.51. The show will culminate in a grand finale that will leave the audience in awe.。

小学上册第十四次英语第3单元暑期作业

小学上册英语第3单元暑期作业英语试题一、综合题(本题有100小题，每小题1分，共100分.每小题不选、错误，均不给分)1.Which planet is known for its extreme temperatures and thick atmosphere?A. MercuryB. VenusC. MarsD. JupiterB2.The _______ (The Great Migration) saw African Americans move north for jobs.3.I have a blue _______ (我有一个蓝色的_______).4.The __________ (历史的共鸣) speaks to us all.5.The first permanent English settlement in America was _______.6.What is the name of the first woman to go into space?A. Sally RideB. Valentina TereshkovaC. Mae JemisonD. Eileen Collins7.I believe that every child should have access to __________.8.n Rainforest is located in __________. (南美洲) The Amaz9.The sun is _____ in the afternoon. (shining)10.What do you call a baby dog?A. KittenB. PuppyC. CubD. CalfB11.The __________ revolutionized transportation during the 19th century. (铁路)12.The park is very ________.13.The study of the properties of substances is known as _______. (物理化学)14.The chemical symbol for selenium is _______.15.The _____ (小鸟) sings sweetly in the morning light.16.Pollinators help flowers to ______ (授粉).17. A ______ is a natural feature that can be explored for research.18.________ (灌溉) is essential in dry areas.19. A ______ can be found in lakes and rivers.20.Newton's first law is about inertia and ______.21.The _____ (花盆) needs drainage holes.22. A solution that contains a large amount of solute is said to be _______.23. A ________ has a long tail and big ears.24. A _______ change alters the appearance but not the chemical composition.25.I love to play with my ________ (玩具火车) on rainy days.26.At school, I have many friends. We often play __________ together during recess. My best friend is __________. We enjoy __________ and __________ after school.27.We go _____ (hiking) in the mountains.28.What do we call the main ingredient in a salad?A. DressingB. LettuceC. CroutonsD. CheeseB29. A compound with a bitter taste is likely a ______.30.The ancient Romans were skilled in _____ and architecture.31.What is the name of the famous fictional detective created by Arthur Conan Doyle?A. Hercule PoirotB. Sherlock HolmesC. Miss MarpleD. Sam SpadeB32. A calorimeter measures the amount of ______ (heat) in a substance.33. A __________ is formed by the movement of glaciers over rock.34.She is wearing a lovely ___. (dress)35.What do you call a baby bison?A. CalfB. PupC. KitD. FawnA36.They are _____ (listening) to music.37.The park is _______ (适合家庭)。

我最喜欢的衣服是一件浅蓝色外套英语作文

In the vast and diverse world of fashion, where trends come and go with the seasons, there exists a particular piece in my wardrobe that transcends fleeting fads - my cherished light blue jacket. This essay serves as an ode to this timeless garment, which has not only become a staple in my personal style but also carries a significant emotional weight and practicality that I find unparalleled.My beloved light blue jacket is a hue that whispers serenity and evokes images of clear skies on a sunny day. The shade is neither too bold nor too timid; it's a perfect balance between making a subtle statement and blending seamlessly into any ensemble. Its color is reminiscent of the calming effect of water or the freshness of a spring morning, creating a sense of tranquility and positive energy every time I wear it.The design of the jacket itself is what sets it apart from other pieces in my wardrobe. It’s a classic cut, tailored to perfection with a blend of modern minimalism and vintage charm. The fit is snug yet comfortable, allowing for ease of movement while still maintaining a flattering silhouette. The lightweight fabric makes it versatile enough to be worn across multiple seasons, transitioning effortlessly from cool spring evenings to breezy summer nights.Moreover, the jacket is adorned with intricate details such as silver buttons, adding a touch of sophistication without overshadowing its overall simplicity. The quality of craftsmanship is evident in the fine stitching, ensuring durability and longevity. This thoughtful construction is a testament to the high standards I uphold when selecting my garments, reflecting my belief in investing in timeless, well-made pieces rather than disposable fast fashion.On a more sentimental note, this jacket holds numerous memories dear to my heart. It was a gift from my grandmother who instilled in me her passion for elegance and simplicity in dressing. Every time I slip it on, I am reminded of her wise words about the power of clothing to express oneself and the importance of being comfortable in one's skin. It encapsulates her legacy, becoming a wearable keepsake that bridges generations.Practically speaking, the light blue jacket's versatility is its mostoutstanding feature. It pairs beautifully with a myriad of outfits – from complementing my casual jeans and t-shirt look to elevating my business casual attire by layering over a dress shirt. Whether I'm heading out for a casual brunch with friends, attending a formal meeting, or setting off on a weekend adventure, it consistently delivers a polished appearance that aligns with my personal aesthetic.Furthermore, the jacket’s adaptability to various occasions speaks volumes about its sustainability aspect. In a world increasingly conscious about the environmental impact of fashion, owning a garment that can serve multiple purposes significantly reduces the need for constant consumption. This light blue jacket stands as a symbol of my commitment to responsible fashion choices.In conclusion, my favorite piece of clothing, the light blue jacket, transcends the boundaries of mere material possession. It is a harmonious fusion of aesthetics, sentimentality, functionality, and ethical considerations. It represents a personal narrative woven into the fabric of my daily life, and it continues to be a cherished companion through the ever-evolving chapters of my style journey. With each wear, it reinforces my belief that true style lies not just in what you wear, but in how it resonates with your identity and values. And in my case, this light blue jacket does so perfectly, making it an irreplaceable treasure in my closet.Word Count: 659 words (excluding title)。

描写自己五官相貌姓名年龄的英语作文

描写自己五官相貌姓名年龄的英语作文To begin with, my eyes are perhaps the most captivating feature of my countenance. They are almond-shaped, reminiscent of the traditional Asian aesthetic, yet they possess a striking hue of emerald green that defies convention. This rare combination imbues them with an enigmatic quality, as if holding untold stories and unspoken emotions. The lashes that fringe them are long and dark, adding depth and allure to their gaze. When illuminated by sunlight or the soft glow of a candle, my eyes sparkle like verdant gems, drawing others into their mysterious depths.My nose, straight and refined, serves as the graceful bridge between my eyes and lips. Its slender profile, neither too prominent nor too diminutive, contributes to the overall balance of my facial structure. It is adorned with a delicate dusting of freckles, a subtle reminder of my mixed heritage and the whimsical nature that resides within me.Moving downwards, my lips are full and softly curved, often described as the embodiment of sensuality and warmth.Their natural rose-pink shade complements my fair complexion, lending a subtle hint of color to my otherwise understated visage. When I smile, my lips part to reveal a set of perfectly aligned teeth, further enhancing the infectious joy that radiates from within.My hair, a lustrous black mane, cascades down to my waist in gentle waves. Its rich texture and sheen reflect my commitment to nurturing its health, while the occasional streaks of auburn highlight add a touch of unpredictability and playfulness. Whether styled in a sleek bun or left loose to dance in the breeze, my hair serves as a crowning glory that accentuates my femininity and grace.In terms of my physique, I stand at a statuesque five feet nine inches, with a slender yet athletic build. My shoulders are broad enough to convey strength and poise, while my hips gently flare, creating an alluring hourglass silhouette. My skin, a pale ivory tone, is smooth and unblemished, bearing testament to my meticulous skincare regimen and the importance I place on self-care.In conclusion, Emily Zhang is more than just a name; it represents a woman who embraces her multifaceted identity, manifesting in her singular appearance. At 26years old, I carry with me the wisdom garnered from life experiences, the audacity of youth, and the unwavering confidence in my own unique beauty. My五官, a harmonious fusion of distinctive elements, serve as a testament to the beauty that arises from embracing diversity and celebrating individuality.。

感知融合算法英语

感知融合算法英语Perceptual Fusion Algorithms.Perceptual fusion algorithms are a subset of artificial intelligence and computer vision techniques that aim to combine multiple sensory inputs, such as visual, auditory, tactile, and olfactory information, to create a unified and enhanced perception of the environment. These algorithms are designed to mimic the way the human brain integrates various sensory signals to form a coherent understanding of the world.At the core of perceptual fusion lies the concept of sensory integration, which involves combining data from different sensors to create a more comprehensive and accurate representation of the surroundings. This integration can occur at various levels, ranging from low-level signal processing to high-level cognitive representations.Low-level sensory integration involves combining raw sensory data from different modalities to create a unified sensory representation. For example, in robotics, visual and tactile sensors can be fused to provide a robot with a more comprehensive understanding of its environment and the objects it interacts with. This integration can help the robot better navigate, grasp, and manipulate objects based on both visual cues and tactile feedback.Mid-Level Sensory Integration.Mid-level sensory integration occurs at the level of feature extraction and representation. Algorithms in this category aim to extract relevant features from different sensory modalities and combine them to create a more robust and discriminative representation. For instance, in speech recognition, audio and video data can be fused to enhance the accuracy of speech transcription by leveraging both auditory and visual cues.High-level sensory integration occurs at the level of cognitive processing and decision-making. In this context, algorithms aim to integrate information from different sensory modalities to form a higher-level understanding of the environment and make informed decisions. Autonomous vehicles, for example, rely on a combination of visual, radar, and lidar sensors to perceive their environment, detect obstacles, and navigate safely.Challenges and Future Directions.Despite the significant progress made in perceptual fusion algorithms, several challenges remain. One of the primary challenges is dealing with the inherent uncertainty and noise present in sensor data. Algorithms need to be robust enough to handle this variability and provide accurate and reliable fusion results.Another challenge lies in the complexity and diversity of real-world environments. Developing algorithms that canadapt to different environments and handle a wide range of sensory inputs is crucial for their widespread application.Future research in perceptual fusion algorithms could focus on improving the accuracy and efficiency of fusion techniques, exploring new modalities such as olfactory and gustatory sensors, and developing more adaptive and robust algorithms that can handle a wide range of environmental conditions.In conclusion, perceptual fusion algorithms play a crucial role in enhancing our understanding of the world by combining multiple sensory inputs. These algorithms havethe potential to revolutionize various fields, including robotics, autonomous vehicles, and speech recognition, by providing a more comprehensive and accurate representationof the environment. With continued research and development, we can expect to see significant advancements in this exciting field of artificial intelligence and computer vision.。

介绍汽车外观英语作文

介绍汽车外观英语作文The sleek silhouette of a car is not just a testament to its engineering prowess, but also a visual symphony thatstirs the heart of every automotive enthusiast. Picture the perfect blend of aerodynamics and design, where every curve and line is meticulously crafted to not only reduce drag but also to captivate the onlooker's gaze. The bold grille, a signature feature, often adorned with chrome or carbon fiber, sets the tone for the vehicle's assertive presence on the road. Headlights, now more than ever, are a fusion of functionality and style, with LED or even laser technology providing a piercing beam that cuts through the night.Moving along the body, the contours and creases tell a story of speed and agility. The side profile showcases the car's dynamic stance, with a low center of gravity and wide wheel arches that hint at the power lurking beneath the surface. The doors, often featuring hidden handles or frameless windows, contribute to the seamless, uninterrupted flow of the car's skin. The rear end is no less impressive, with taillights that stretch across the width, creating a broad and stable appearance. The rear diffuser and exhaust tips are the final touches, suggesting the car's performance capabilities.The wheels, a crucial element of the car's aesthetic, range from the classic alloy to the more aggressive, sporty designs that feature spokes and intricate patterns. They notonly support the vehicle but also enhance its overall look, with larger diameters and wider tires often chosen for their performance benefits and visual impact.In essence, the exterior of a car is a canvas where form meets function, where every detail is a reflection of the manufacturer's dedication to both beauty and performance.It's a language that speaks volumes, a language that every car lover can appreciate and understand. Whether it's the classic elegance of a vintage model or the cutting-edge innovation of a modern supercar, the exterior design of a car is a visual treat that never fails to inspire awe and admiration.。

决策层特征融合decision level identity fusion

Sensor N
Identity Declaration
Identity Declaration
Identity Declaration
Decision Level
Fusion – Identity Fusion
Introduction
Decision-Level Fusion Techniques
Decision-Level Identity Fusion
Tan Xin
Lab 5, System Engineering Dept.
Contents
1. Introduction 2. Classical inference 3. Bayesian inference 4. Dempster-Shafer’s method* 5. Generalized Evidence Processing (GEP) Theory 6. Heuristic methods for identity fusion 7. Implementation and trade-offs
Feature-level fusion (Feature extraction, identity declaration)
Data-level fusion
(Data fused)
Feature Extraction Association
Introduction
Sensor A
Sensor B
lim P( A) fn( A) here fn( A) k
n
n
n trials, occurrence of k times
Classical inference
One disadvantage