Stationary Iterative Method
丘赛计算与应用数学考试大纲
丘赛计算与应⽤数学考试⼤纲原⽂地址：Computational MathematicsInterpolation and approximationPolynomial interpolation and least square approximation; trigonometric interpolation and approximation, fast Fourier transform; approximations by rational functions; splines.Nonlinear equation solversConvergence of iterative methods (bisection, secant method, Newton method, other iterative methods) for both scalar equations and systems; finding rootsof polynomials.Linear systems and eigenvalue problemsDirect solvers (Gauss elimination, LU decomposition, pivoting, operation count, banded matrices, round-off error accumulation); iterative solvers (Jacobi, Gauss-Seidel, successive over-relaxation, conjugate gradient method, multi-grid method, Krylov methods); numerical solutions for eigenvalues and eigenvectorsNumerical solutions of ordinary differential equationsOne step methods (Taylor series method and Runge-Kutta method); stability, accuracy and convergence; absolute stability, long time behavior; multi-stepmethodsNumerical solutions of partial differential equationsFinite difference method; stability, accuracy and convergence, Lax equivalence theorem; finite element method, boundary value problemsReferences:[1] C. de Boor and S.D. Conte, Elementary Numerical Analysis, an algorithmicapproach, McGraw-Hill, 2000.[2] G.H. Golub and C.F. van Loan, Matrix Computations, third edition, JohnsHopkins University Press, 1996.[3] E. Hairer, P. Syvert and G. Wanner, Solving Ordinary Differential Equations, Springer, 1993.[4] B. Gustafsson, H.-O. Kreiss and J. Oliger, Time Dependent Problems and Difference Methods, John Wiley Sons, 1995.[5] G. Strang and G. Fix, An Analysis of the Finite Element Method, second edition, Wellesley-Cambridge Press, 2008.Applied MathematicsODE with constant coefficients; Nonlinear ODE: critical points, phase space& stability analysis; Hamiltonian, gradient, conservative ODE's.Calculus of Variations: Euler-Lagrange Equations; Boundary Conditions, parametric formulation; optimal control and Hamiltonian, Pontryagin maximum principle.First order partial differential equations (PDE) and method of characteristics; Heat, wave, and Laplace's equation; Separation of variables and eigen-function expansions; Stationary phase method; Homogenization method for elliptic and linear hyperbolic PDEs; Homogenization and front propagation of Hamil ton-Jacobi equations; Geometric optics for dispersive wave equations. References:W.D. Boyce and R.C. DiPrima, Elementary Differential Equations, Wiley, 2009 F.Y.M. Wan, Introduction to Calculus of Variations and Its Applications, Cha pman & Hall, 1995G. Whitham, "Linear and Nonlinear Waves", John-Wiley and Sons, 1974.J. Keener, "Principles of Applied Mathematics", Addison-Wesley, 1988.A. Benssousan, P-L Lions, G. Papanicolaou, "Asymptotic Analysis for Periodic Structures", North-Holland Publishing Co, 1978.V. Jikov, S. Kozlov, O. Oleinik, "Homogenization of differential operators and integral functions", Springer, 1994.J. Xin, "An Introduction to Fronts in Random Media", Surveys and Tutorials in Applied Math Sciences, No. 5, Springer, 2009。
英文单词
Synopsis:概要，大纲Macroscopic:宏观的，肉眼可见的Interconnected:连通的，有联系的Inter-dendritic:晶间Ingots:钢锭，铸块Globular:球状的Substantially:实质上，大体上，充分地Qualitatively:定性地Bulging:膨胀，凸出，打气，折皱（在连铸中是鼓肚的意思！）Hydrogen induced cracking:氢致裂纹（HIC）Correlated to:相互关联Perform:完成，执行Bulk concentration:体积浓度Introduction:引言Accordingly:因此，相应地Countermeasure:对策，对抗措施Equiaxed crystal:等轴晶Aggromerate:聚合Permeability:渗透性Slab:厚板Plate:薄板Contraction:收缩，紧缩Conventional:传统的Inconsistency:不一致Susceptibility:敏感性Resolve:解决，分解Morphology:形态Interpret:解释，解读Areal fraction:面积分数Quench:淬火Dendrite tips:枝晶尖端Specimen:试样，样品Proportional:成比例的Coarsening:晶粒粗大Coalescence:合并，联合Nevertheless:不过，虽然如此Planar:二维的，平面的Cellular：细胞的，多孔的Interface:界面，接触面Refer to :适用于Constant：常量Approximation:近似值，近似法Apparatus:仪器，装置Diagram:图表，图解Derive from:源自，来自Longitudinal:纵向的，长度的Section:截面Magnification:放大率schematic:图解的curvature:弯曲arrowed:标有箭头的in essence:本质上，其实lagged 延迟radial heat 辐射热transient:短暂的crucible：坩埚internal diam:内部直径chromel alumel thermocouple:铬镍-铝镍热电偶allumina:氧化铝agitated ice water:激冷冰水given:考虑到electropolish:用电解法抛光transverse:横向的，横断的metallographic:金相的diffusion:扩散，传播coefficient:系数，率undercooling/supercooling:过冷interdendritic:枝晶间，树枝晶间的intragranular:晶内的granular:颗粒的，粒状的isotherm:等温线arc-welded:弧焊deposit:沉积物，存款inversely:相反地geometry:几何学justification:理由，辩护，认为正当somewhat:有点gradient:梯度，倾斜度recognised:承认，辨别substitute:代替exponent:指数excluding:不包括，将。
影响PETCT图像质量和诊断的诸因素分析
探讨影响PET/CT图像质量及诊断的多种 因素的目的
了解影响PET/CT图像的各种因素
如何获得高质量的PET/CT图像
如何综合PET和CT的图像信息，提高诊 断准确性，尽量减少错误诊断
商品化的PET/CT扫描仪
GE公司
Discovery LS Discovery ST
西门子公司
Biograph HR 、 Biograph HS、 Biograph32 、 Biograph 64
PMT gain图
南方PET中心
Normalization Calibration 1）探测器归一化校准 ，用以校正发射显像资料
探头归一化也称为探测器灵敏度校正。 对PET数据进行图像重建时，基本假设是符合投影线灵敏度一致。 探测器参数的差异，符合投影线与探测器表面夹角不同，都会造 成灵敏度的差异。 对这些造成探测灵敏度差异的因素，进行校正的过程称为探测器 归一化。 对每一条符合投影线来说，都应有一个归一化因子
Six Detector Blocks（nodule)
Detector Cassette
One Block
18环探测器的组成
18 Rings 672 Crystals/Ring
南方PET中心
校准（Calibration）
Blank Scan: 5min, daily Singles /update gain adjustment: 1hr,weekly Coincidence Timing Calibration: 5min,
飞利浦
GEMINI
世界上著名的医疗仪器大公司的参与开发，使 PET/CT的发展突飞猛进
PET/CT组成
PET/CT扫描仪
功能 影像 与形 态影 像的 优化 组合
控制理论术语中英文对照表
附录Ⅲ 控制理论术语中英文对照表AAbsolute error 绝对误差Accuracy 精确度Active electric network 有源网络Actuating signal 作用信号，启动信号Actuator 执行机构，调节器，激励器Adjust 调整Adaptive control 自适应控制Amplitude 振幅，幅值Analog computer 模拟计算机Analog signal 模拟信号Angle condition 相角条件Angle of arrival 入射角Angle of departure 出射角Angular acceleration 角加速度Argument 辐角Asymptote 渐近线Asymptotically stable 渐近稳定的Automatic control 自动控制Attenuation 衰减Auxiliary equation 辅助方程BBacklash 间隙，回差Bandwidth 带宽Bang-bang control 砰-砰控制，继电控制Biocybernetics 生物控制论Block diagram 方框图，方块图，结构图Bode plot 波特图Breakaway-points 分离点CCAD(computer aided design) 计算机辅助设计Cascade compensation 串联补偿(校正)Cascade control 串级控制Channel 通道Characteristic equation 特征方程Classical control theory 古典控制理论Closed loop control system 闭环控制系统Closed loop frequency response 闭环频率响应Closed loop pole 闭环极点Closed loop zero 闭环零点Combinational control system 复合控制系统Comparator 比较器Comparing element 比较元件，比较环节Compound control 复合控制Compensation 补偿，校正Complex plane 复平面Conditional stability 条件稳定Configuration 结构，配置，方案，组态Constant M loci 等M圆Continuous system 连续系统Controlled variable 被控变量Control system 控制系统Control valve 调节阀Controllability 可控性，能控性Corner frequency 转折频率，交接频率Correcting unit 校正器Correction 校正Coupling 耦合Criterion 判据，准则Critical damping 临界阻尼Cut-off frequency 截止频率Cybernetics 控制论DDamped natural frequency 有阻尼自然频率Damper 阻尼器Damping factor 阻尼系数Damping ratio 阻尼比Dead band 死区Dead time 纯延迟，延迟时间Decomposition 分解Delay 滞后Delay element 滞后环节Derivation action 微分作用Derivative control 微分控制Desired value 预期值，期望值Deviation 偏差Differencing junction 比较点Differential equations 微分方程Digital computer 数字计算机Discrete-data system 离散数据系统Disturbance 扰动，干扰Dominant pole 主导极点Duality 对偶性Dynamic equation 动态方程Dynamic error 动态误差Dynamic process 动态过程EEquilibrium state 平衡状态Eigenvalue 特征值Eigenvector 特征向量Error 误差Error coefficient 误差系数Error signal 误差信号Even symmetry 偶对称Exponential 指数，指数的，幂的Extremum 极值FFeedback 反馈Feedback control 反馈控制Feedback element 反馈环节Feedback path 反馈通道Feedforward 前馈Final value 终值First-order system 一阶系统Forward path 前向通道Frequency 频率Frequency domain 频域Frequency response 频率响应Frequency response characteristic 频率响应特性GGain 增益Gain margin 增益裕度，幅值裕度HHarmonic response 谐波响应Holder 保持器Homogeneous equation 齐次方程Hurwitz determinant 赫尔维茨行列式Hysteresis error 回差IIdealized system 理想化系统Identification 辨识Impulse response 脉冲响应Inertial 惯性的，惯量的，惰性的Inherent characteristic 固有特性Initial condition 初始条件Initial state 初始状态Initial value theorem 初值定理Inner loop 内环Input 输入Input node 输入节点Input signal 输入信号Integral action 积分作用Integral control 积分控制Internal description 内部描述Inverse matrix 逆矩阵Inverse transformation 反变换Inverse Laplace transforms 拉普拉斯反变换Isocline method 等倾线法Iterative algorithm 迭代算法JJordan block 约当块Jordan canonical form 约当标准型KKalman criterion 卡尔曼准则Kalman filter 卡尔曼滤波器LLag network 滞后网络Lag compensation 滞后补偿Laplace transforms 拉普拉斯变换Large scale system 大系统Lead network 超前网络Limit cycle 极限环Linearization 线性化Linearity 线性度Linear equation 线性方程Linear system 线性系统Load-response curve 负荷响应曲线Locus 轨迹Log magnitude 对数幅值Low pass characteristic 低通特性MMagnitude condition 幅值条件Magnitude-Versus-Phase plot 幅值特性曲线Manipulated variable 操纵变量Mason rule 梅森公式Mathematical model 数学模型Matrix 矩阵Maximum overshoot 最大超调量Measurable 可测量的Measured variable 被测变量Minimum phase system 最小相位系统Model decomposition 模型分解Modulus 模Moment of inertia 转动惯量Multinomial 多项式（的）Multivariable system 多变量系统NNatural frequency 自然频率Negative feedback 负反馈Nichols chart 尼柯尔斯图线Node 节点Noise 噪声Nonlinear control system 非线性控制系统Nonminimum phase system 非最小相位系统Nonsingular 非奇异的Norm 范数Numerical control 数字控制Nyquist criterion 奈奎斯特判据Nyquist contour 奈奎斯特轨线OObjective function 目标函数Observability 可观性，能观性Observer 观测器Odd symmetry 奇对称Offset 偏移，位移Open loop 开环Optimal control 最优控制Optimization 最优化Origin 原点Oscillation 振荡Oscillatory response 振荡响应Outer loop 外环Output 输出Output signal 输出信号Over damping 过阻尼Overshoot 超调量PParameter 参数Peak overshoot 超调峰值Peak time 峰值时间Performance index 性能指标Phase lag 相位滞后Phase lead 相位超前Phase margin 相角裕度Phase plane 相平面Pickoff point 引出点PID(proportional plus integral plus derivative) PID(比例、积分、微分)控制器Piece-wise linearization 分段线性化Pole 极点Pole assignment 极点配置Polynomial 多项式Position error 位置误差Positive definiteness 正定性Pre-compensator 预补偿器Process control 过程控制Proportional band 比例带Proportional control 比例控制Pulse 脉冲Pulse width 脉宽Pure delay 纯滞后RRamp input 斜坡输入Ramp response 斜坡响应Rate feedback 速度反馈Rate time 微分时间，预调时间Rational 有理(数)的，合理的Realization 实现Reference variable 参考变量Regulator 调节器Relative stability 相对稳定Reliability 可靠性Response 响应Reset time 再调时间，积分时间Residue 留数Rise time 上升时间Roots loci 根轨迹Routh-Hurwitz criterion 劳斯-赫尔维茨判据Routh stability criterion 劳斯稳定判据SSampling control 采样控制Sampling freqency 采样频率Sampling period 采样周期Series compensation 串联补偿Servo 伺服机构，伺服电机Servodrive 伺服传动，伺服转动装置Set value 设定值Settling time 调节时间，稳定时间Signal flow graph 信号流图Singular point 奇点Stability 稳定（性）Stability margin 稳定裕度State equations 状态方程State space 状态空间State variables 状态变量Steady-state 稳态的Stationary 稳态Steady-state deviation 稳态偏差Steady-state error 稳态误差Step singal 阶跃信号Step response 阶跃响应Stochastic process 随机过程Summing junction 相加点Superposition 叠加Systematic deviation 系统偏差System identification 系统辨识TTangent 切线Threshold value 阈值Time constant 时间常数Time domain 时域Time response 时间响应Time-invariant system 常定(时不变)系统Time-varying system 时变系统Trajectory 轨迹Transducer 传感器，变换器Transfer function 传递函数Transfer matrix 转移矩阵Transient response 暂态响应Transmitter 变送器Transportation lag 传输滞后UUndamped natural frequency 无阻尼自然频率Underdamping 欠阻尼Uniform stability 一致稳定Unit circle 单位圆Unit impulse 单位脉冲Unit step function 单位阶跃函数Unity feedback 单位反馈Unity matrix 单位矩阵Unstable 不稳定的Asymmetrical 不对称的VValue of quantity 量值variable 变量Vector 向量Velocity feedback 速度反馈WWaveform 波形Weighting function 加权函数White noise 白噪声ZZero 零点Zero input response 零点输入响应Zero-order holder 零阶保持器Zero-state response 零状态响应Z-transfer function Z传递函数Z-transformation Z变换。
人工智能词汇
常用英语词汇 -andrew Ng课程average firing rate均匀激活率intensity强度average sum-of-squares error均方差Regression回归backpropagation后向流传Loss function损失函数basis 基non-convex非凸函数basis feature vectors特点基向量neural network神经网络batch gradient ascent批量梯度上涨法supervised learning监察学习Bayesian regularization method贝叶斯规则化方法regression problem回归问题办理的是连续的问题Bernoulli random variable伯努利随机变量classification problem分类问题bias term偏置项discreet value失散值binary classfication二元分类support vector machines支持向量机class labels种类标记learning theory学习理论concatenation级联learning algorithms学习算法conjugate gradient共轭梯度unsupervised learning无监察学习contiguous groups联通地区gradient descent梯度降落convex optimization software凸优化软件linear regression线性回归convolution卷积Neural Network神经网络cost function代价函数gradient descent梯度降落covariance matrix协方差矩阵normal equations DC component直流重量linear algebra线性代数decorrelation去有关superscript上标degeneracy退化exponentiation指数demensionality reduction降维training set训练会合derivative导函数training example训练样本diagonal对角线hypothesis假定，用来表示学习算法的输出diffusion of gradients梯度的弥散LMS algorithm “least mean squares最小二乘法算eigenvalue特点值法eigenvector特点向量batch gradient descent批量梯度降落error term残差constantly gradient descent随机梯度降落feature matrix特点矩阵iterative algorithm迭代算法feature standardization特点标准化partial derivative偏导数feedforward architectures前馈构造算法contour等高线feedforward neural network前馈神经网络quadratic function二元函数feedforward pass前馈传导locally weighted regression局部加权回归fine-tuned微调underfitting欠拟合first-order feature一阶特点overfitting过拟合forward pass前向传导non-parametric learning algorithms无参数学习算forward propagation前向流传法Gaussian prior高斯先验概率parametric learning algorithm参数学习算法generative model生成模型activation激活值gradient descent梯度降落activation function激活函数Greedy layer-wise training逐层贪心训练方法additive noise加性噪声grouping matrix分组矩阵autoencoder自编码器Hadamard product阿达马乘积Autoencoders自编码算法Hessian matrix Hessian矩阵hidden layer隐含层hidden units隐蔽神经元Hierarchical grouping层次型分组higher-order features更高阶特点highly non-convex optimization problem高度非凸的优化问题histogram直方图hyperbolic tangent双曲正切函数hypothesis估值，假定identity activation function恒等激励函数IID 独立同散布illumination照明inactive克制independent component analysis独立成份剖析input domains输入域input layer输入层intensity亮度/灰度intercept term截距KL divergence相对熵KL divergence KL分别度k-Means K-均值learning rate学习速率least squares最小二乘法linear correspondence线性响应linear superposition线性叠加line-search algorithm线搜寻算法local mean subtraction局部均值消减local optima局部最优解logistic regression逻辑回归loss function损失函数low-pass filtering低通滤波magnitude幅值MAP 极大后验预计maximum likelihood estimation极大似然预计mean 均匀值MFCC Mel 倒频系数multi-class classification多元分类neural networks神经网络neuron 神经元Newton’s method牛顿法non-convex function非凸函数non-linear feature非线性特点norm 范式norm bounded有界范数norm constrained范数拘束normalization归一化numerical roundoff errors数值舍入偏差numerically checking数值查验numerically reliable数值计算上稳固object detection物体检测objective function目标函数off-by-one error缺位错误orthogonalization正交化output layer输出层overall cost function整体代价函数over-complete basis超齐备基over-fitting过拟合parts of objects目标的零件part-whole decompostion部分-整体分解PCA 主元剖析penalty term处罚因子per-example mean subtraction逐样本均值消减pooling池化pretrain预训练principal components analysis主成份剖析quadratic constraints二次拘束RBMs 受限 Boltzman 机reconstruction based models鉴于重构的模型reconstruction cost重修代价reconstruction term重构项redundant冗余reflection matrix反射矩阵regularization正则化regularization term正则化项rescaling缩放robust 鲁棒性run 行程second-order feature二阶特点sigmoid activation function S型激励函数significant digits有效数字singular value奇怪值singular vector奇怪向量smoothed L1 penalty光滑的L1 范数处罚Smoothed topographic L1 sparsity penalty光滑地形L1 稀少处罚函数smoothing光滑Softmax Regresson Softmax回归sorted in decreasing order降序摆列source features源特点Adversarial Networks抗衡网络sparse autoencoder消减归一化Affine Layer仿射层Sparsity稀少性Affinity matrix亲和矩阵sparsity parameter稀少性参数Agent 代理 /智能体sparsity penalty稀少处罚Algorithm 算法square function平方函数Alpha- beta pruningα - β剪枝squared-error方差Anomaly detection异样检测stationary安稳性（不变性）Approximation近似stationary stochastic process安稳随机过程Area Under ROC Curve／ AUC Roc 曲线下边积step-size步长值Artificial General Intelligence/AGI通用人工智supervised learning监察学习能symmetric positive semi-definite matrix Artificial Intelligence/AI人工智能对称半正定矩阵Association analysis关系剖析symmetry breaking对称无效Attention mechanism注意力体制tanh function双曲正切函数Attribute conditional independence assumptionthe average activation均匀活跃度属性条件独立性假定the derivative checking method梯度考证方法Attribute space属性空间the empirical distribution经验散布函数Attribute value属性值the energy function能量函数Autoencoder自编码器the Lagrange dual拉格朗日对偶函数Automatic speech recognition自动语音辨别the log likelihood对数似然函数Automatic summarization自动纲要the pixel intensity value像素灰度值Average gradient均匀梯度the rate of convergence收敛速度Average-Pooling均匀池化topographic cost term拓扑代价项Backpropagation Through Time经过时间的反向流传topographic ordered拓扑次序Backpropagation/BP反向流传transformation变换Base learner基学习器translation invariant平移不变性Base learning algorithm基学习算法trivial answer平庸解Batch Normalization/BN批量归一化under-complete basis不齐备基Bayes decision rule贝叶斯判断准则unrolling组合扩展Bayes Model Averaging／ BMA 贝叶斯模型均匀unsupervised learning无监察学习Bayes optimal classifier贝叶斯最优分类器variance 方差Bayesian decision theory贝叶斯决议论vecotrized implementation向量化实现Bayesian network贝叶斯网络vectorization矢量化Between-class scatter matrix类间散度矩阵visual cortex视觉皮层Bias 偏置 /偏差weight decay权重衰减Bias-variance decomposition偏差 - 方差分解weighted average加权均匀值Bias-Variance Dilemma偏差–方差窘境whitening白化Bi-directional Long-Short Term Memory/Bi-LSTMzero-mean均值为零双向长短期记忆Accumulated error backpropagation积累偏差逆传Binary classification二分类播Binomial test二项查验Activation Function激活函数Bi-partition二分法Adaptive Resonance Theory/ART自适应谐振理论Boltzmann machine玻尔兹曼机Addictive model加性学习Bootstrap sampling自助采样法／可重复采样Bootstrapping自助法Break-Event Point／ BEP 均衡点Calibration校准Cascade-Correlation级联有关Categorical attribute失散属性Class-conditional probability类条件概率Classification and regression tree/CART分类与回归树Classifier分类器Class-imbalance类型不均衡Closed -form闭式Cluster簇/ 类/ 集群Cluster analysis聚类剖析Clustering聚类Clustering ensemble聚类集成Co-adapting共适应Coding matrix编码矩阵COLT 国际学习理论会议Committee-based learning鉴于委员会的学习Competitive learning竞争型学习Component learner组件学习器Comprehensibility可解说性Computation Cost计算成本Computational Linguistics计算语言学Computer vision计算机视觉Concept drift观点漂移Concept Learning System /CLS观点学习系统Conditional entropy条件熵Conditional mutual information条件互信息Conditional Probability Table／ CPT 条件概率表Conditional random field/CRF条件随机场Conditional risk条件风险Confidence置信度Confusion matrix混杂矩阵Connection weight连结权Connectionism 连结主义Consistency一致性／相合性Contingency table列联表Continuous attribute连续属性Convergence收敛Conversational agent会话智能体Convex quadratic programming凸二次规划Convexity凸性Convolutional neural network/CNN卷积神经网络Co-occurrence同现Correlation coefficient有关系数Cosine similarity余弦相像度Cost curve成本曲线Cost Function成本函数Cost matrix成本矩阵Cost-sensitive成本敏感Cross entropy交错熵Cross validation交错考证Crowdsourcing众包Curse of dimensionality维数灾害Cut point截断点Cutting plane algorithm割平面法Data mining数据发掘Data set数据集Decision Boundary决议界限Decision stump决议树桩Decision tree决议树／判断树Deduction演绎Deep Belief Network深度信念网络Deep Convolutional Generative Adversarial NetworkDCGAN深度卷积生成抗衡网络Deep learning深度学习Deep neural network/DNN深度神经网络Deep Q-Learning深度Q 学习Deep Q-Network深度Q 网络Density estimation密度预计Density-based clustering密度聚类Differentiable neural computer可微分神经计算机Dimensionality reduction algorithm降维算法Directed edge有向边Disagreement measure不合胸怀Discriminative model鉴别模型Discriminator鉴别器Distance measure距离胸怀Distance metric learning距离胸怀学习Distribution散布Divergence散度Diversity measure多样性胸怀／差别性胸怀Domain adaption领域自适应Downsampling下采样D-separation（ Directed separation）有向分别Dual problem对偶问题Dummy node 哑结点General Problem Solving通用问题求解Dynamic Fusion 动向交融Generalization泛化Dynamic programming动向规划Generalization error泛化偏差Eigenvalue decomposition特点值分解Generalization error bound泛化偏差上界Embedding 嵌入Generalized Lagrange function广义拉格朗日函数Emotional analysis情绪剖析Generalized linear model广义线性模型Empirical conditional entropy经验条件熵Generalized Rayleigh quotient广义瑞利商Empirical entropy经验熵Generative Adversarial Networks/GAN生成抗衡网Empirical error经验偏差络Empirical risk经验风险Generative Model生成模型End-to-End 端到端Generator生成器Energy-based model鉴于能量的模型Genetic Algorithm/GA遗传算法Ensemble learning集成学习Gibbs sampling吉布斯采样Ensemble pruning集成修剪Gini index基尼指数Error Correcting Output Codes／ ECOC纠错输出码Global minimum全局最小Error rate错误率Global Optimization全局优化Error-ambiguity decomposition偏差 - 分歧分解Gradient boosting梯度提高Euclidean distance欧氏距离Gradient Descent梯度降落Evolutionary computation演化计算Graph theory图论Expectation-Maximization希望最大化Ground-truth实情／真切Expected loss希望损失Hard margin硬间隔Exploding Gradient Problem梯度爆炸问题Hard voting硬投票Exponential loss function指数损失函数Harmonic mean 调解均匀Extreme Learning Machine/ELM超限学习机Hesse matrix海塞矩阵Factorization因子分解Hidden dynamic model隐动向模型False negative假负类Hidden layer隐蔽层False positive假正类Hidden Markov Model/HMM 隐马尔可夫模型False Positive Rate/FPR假正例率Hierarchical clustering层次聚类Feature engineering特点工程Hilbert space希尔伯特空间Feature selection特点选择Hinge loss function合页损失函数Feature vector特点向量Hold-out 留出法Featured Learning特点学习Homogeneous 同质Feedforward Neural Networks/FNN前馈神经网络Hybrid computing混杂计算Fine-tuning微调Hyperparameter超参数Flipping output翻转法Hypothesis假定Fluctuation震荡Hypothesis test假定考证Forward stagewise algorithm前向分步算法ICML 国际机器学习会议Frequentist频次主义学派Improved iterative scaling/IIS改良的迭代尺度法Full-rank matrix满秩矩阵Incremental learning增量学习Functional neuron功能神经元Independent and identically distributed/独Gain ratio增益率立同散布Game theory博弈论Independent Component Analysis/ICA独立成分剖析Gaussian kernel function高斯核函数Indicator function指示函数Gaussian Mixture Model高斯混杂模型Individual learner个体学习器Induction归纳Inductive bias归纳偏好Inductive learning归纳学习Inductive Logic Programming／ ILP归纳逻辑程序设计Information entropy信息熵Information gain信息增益Input layer输入层Insensitive loss不敏感损失Inter-cluster similarity簇间相像度International Conference for Machine Learning/ICML国际机器学习大会Intra-cluster similarity簇内相像度Intrinsic value固有值Isometric Mapping/Isomap等胸怀映照Isotonic regression平分回归Iterative Dichotomiser迭代二分器Kernel method核方法Kernel trick核技巧Kernelized Linear Discriminant Analysis／KLDA核线性鉴别剖析K-fold cross validation k折交错考证／k 倍交错考证K-Means Clustering K–均值聚类K-Nearest Neighbours Algorithm/KNN K近邻算法Knowledge base 知识库Knowledge Representation知识表征Label space标记空间Lagrange duality拉格朗日对偶性Lagrange multiplier拉格朗日乘子Laplace smoothing拉普拉斯光滑Laplacian correction拉普拉斯修正Latent Dirichlet Allocation隐狄利克雷散布Latent semantic analysis潜伏语义剖析Latent variable隐变量Lazy learning懒散学习Learner学习器Learning by analogy类比学习Learning rate学习率Learning Vector Quantization/LVQ学习向量量化Least squares regression tree最小二乘回归树Leave-One-Out/LOO留一法linear chain conditional random field线性链条件随机场Linear Discriminant Analysis／ LDA 线性鉴别剖析Linear model线性模型Linear Regression线性回归Link function联系函数Local Markov property局部马尔可夫性Local minimum局部最小Log likelihood对数似然Log odds／ logit对数几率Logistic Regression Logistic回归Log-likelihood对数似然Log-linear regression对数线性回归Long-Short Term Memory/LSTM 长短期记忆Loss function损失函数Machine translation/MT机器翻译Macron-P宏查准率Macron-R宏查全率Majority voting绝对多半投票法Manifold assumption流形假定Manifold learning流形学习Margin theory间隔理论Marginal distribution边沿散布Marginal independence边沿独立性Marginalization边沿化Markov Chain Monte Carlo/MCMC马尔可夫链蒙特卡罗方法Markov Random Field马尔可夫随机场Maximal clique最大团Maximum Likelihood Estimation/MLE极大似然预计／极大似然法Maximum margin最大间隔Maximum weighted spanning tree最大带权生成树Max-Pooling 最大池化Mean squared error均方偏差Meta-learner元学习器Metric learning胸怀学习Micro-P微查准率Micro-R微查全率Minimal Description Length/MDL最小描绘长度Minimax game极小极大博弈Misclassification cost误分类成本Mixture of experts混杂专家Momentum 动量Moral graph道德图／正直图Multi-class classification多分类Multi-document summarization多文档纲要One shot learning一次性学习Multi-layer feedforward neural networks One-Dependent Estimator／ ODE 独依靠预计多层前馈神经网络On-Policy在策略Multilayer Perceptron/MLP多层感知器Ordinal attribute有序属性Multimodal learning多模态学习Out-of-bag estimate包外预计Multiple Dimensional Scaling多维缩放Output layer输出层Multiple linear regression多元线性回归Output smearing输出调制法Multi-response Linear Regression／ MLR Overfitting过拟合／过配多响应线性回归Oversampling 过采样Mutual information互信息Paired t-test成对 t查验Naive bayes 朴实贝叶斯Pairwise 成对型Naive Bayes Classifier朴实贝叶斯分类器Pairwise Markov property成对马尔可夫性Named entity recognition命名实体辨别Parameter参数Nash equilibrium纳什均衡Parameter estimation参数预计Natural language generation/NLG自然语言生成Parameter tuning调参Natural language processing自然语言办理Parse tree分析树Negative class负类Particle Swarm Optimization/PSO粒子群优化算法Negative correlation负有关法Part-of-speech tagging词性标明Negative Log Likelihood负对数似然Perceptron感知机Neighbourhood Component Analysis/NCA Performance measure性能胸怀近邻成分剖析Plug and Play Generative Network即插即用生成网Neural Machine Translation神经机器翻译络Neural Turing Machine神经图灵机Plurality voting相对多半投票法Newton method牛顿法Polarity detection极性检测NIPS 国际神经信息办理系统会议Polynomial kernel function多项式核函数No Free Lunch Theorem／ NFL 没有免费的午饭定理Pooling池化Noise-contrastive estimation噪音对照预计Positive class正类Nominal attribute列名属性Positive definite matrix正定矩阵Non-convex optimization非凸优化Post-hoc test后续查验Nonlinear model非线性模型Post-pruning后剪枝Non-metric distance非胸怀距离potential function势函数Non-negative matrix factorization非负矩阵分解Precision查准率／正确率Non-ordinal attribute无序属性Prepruning 预剪枝Non-Saturating Game非饱和博弈Principal component analysis/PCA主成分剖析Norm 范数Principle of multiple explanations多释原则Normalization归一化Prior 先验Nuclear norm核范数Probability Graphical Model概率图模型Numerical attribute数值属性Proximal Gradient Descent/PGD近端梯度降落Letter O Pruning剪枝Objective function目标函数Pseudo-label伪标记Oblique decision tree斜决议树Quantized Neural Network量子化神经网络Occam’s razor奥卡姆剃刀Quantum computer 量子计算机Odds 几率Quantum Computing量子计算Off-Policy离策略Quasi Newton method拟牛顿法Radial Basis Function／ RBF 径向基函数Random Forest Algorithm随机丛林算法Random walk随机闲步Recall 查全率／召回率Receiver Operating Characteristic/ROC受试者工作特点Rectified Linear Unit/ReLU线性修正单元Recurrent Neural Network循环神经网络Recursive neural network递归神经网络Reference model 参照模型Regression回归Regularization正则化Reinforcement learning/RL加强学习Representation learning表征学习Representer theorem表示定理reproducing kernel Hilbert space/RKHS重生核希尔伯特空间Re-sampling重采样法Rescaling再缩放Residual Mapping残差映照Residual Network残差网络Restricted Boltzmann Machine/RBM受限玻尔兹曼机Restricted Isometry Property/RIP限制等距性Re-weighting重赋权法Robustness稳重性 / 鲁棒性Root node根结点Rule Engine规则引擎Rule learning规则学习Saddle point鞍点Sample space样本空间Sampling采样Score function评分函数Self-Driving自动驾驶Self-Organizing Map／ SOM自组织映照Semi-naive Bayes classifiers半朴实贝叶斯分类器Semi-Supervised Learning半监察学习semi-Supervised Support Vector Machine半监察支持向量机Sentiment analysis感情剖析Separating hyperplane分别超平面Sigmoid function Sigmoid函数Similarity measure相像度胸怀Simulated annealing模拟退火Simultaneous localization and mapping同步定位与地图建立Singular Value Decomposition奇怪值分解Slack variables废弛变量Smoothing光滑Soft margin软间隔Soft margin maximization软间隔最大化Soft voting软投票Sparse representation稀少表征Sparsity稀少性Specialization特化Spectral Clustering谱聚类Speech Recognition语音辨别Splitting variable切分变量Squashing function挤压函数Stability-plasticity dilemma可塑性 - 稳固性窘境Statistical learning统计学习Status feature function状态特点函Stochastic gradient descent随机梯度降落Stratified sampling分层采样Structural risk构造风险Structural risk minimization/SRM构造风险最小化Subspace子空间Supervised learning监察学习／有导师学习support vector expansion支持向量展式Support Vector Machine/SVM支持向量机Surrogat loss代替损失Surrogate function代替函数Symbolic learning符号学习Symbolism符号主义Synset同义词集T-Distribution Stochastic Neighbour Embeddingt-SNE T–散布随机近邻嵌入Tensor 张量Tensor Processing Units/TPU张量办理单元The least square method最小二乘法Threshold阈值Threshold logic unit阈值逻辑单元Threshold-moving阈值挪动Time Step时间步骤Tokenization标记化Training error训练偏差Training instance训练示例／训练例Transductive learning直推学习Transfer learning迁徙学习Treebank树库algebra线性代数Tria-by-error试错法asymptotically无症状的True negative真负类appropriate适合的True positive真切类bias 偏差True Positive Rate/TPR真切例率brevity简洁，简洁；短暂Turing Machine图灵机[800 ] broader宽泛Twice-learning二次学习briefly简洁的Underfitting欠拟合／欠配batch 批量Undersampling欠采样convergence收敛，集中到一点Understandability可理解性convex凸的Unequal cost非均等代价contours轮廓Unit-step function单位阶跃函数constraint拘束Univariate decision tree单变量决议树constant常理Unsupervised learning无监察学习／无导师学习commercial商务的Unsupervised layer-wise training无监察逐层训练complementarity增补Upsampling上采样coordinate ascent同样级上涨Vanishing Gradient Problem梯度消逝问题clipping剪下物；剪报；修剪Variational inference变分推测component重量；零件VC Theory VC维理论continuous连续的Version space版本空间covariance协方差Viterbi algorithm维特比算法canonical正规的，正则的Von Neumann architecture冯· 诺伊曼架构concave非凸的Wasserstein GAN/WGAN Wasserstein生成抗衡网络corresponds相切合；相当；通讯Weak learner弱学习器corollary推论Weight权重concrete详细的事物，实在的东西Weight sharing权共享cross validation交错考证Weighted voting加权投票法correlation互相关系Within-class scatter matrix类内散度矩阵convention商定Word embedding词嵌入cluster一簇Word sense disambiguation词义消歧centroids质心，形心Zero-data learning零数据学习converge收敛Zero-shot learning零次学习computationally计算(机)的approximations近似值calculus计算arbitrary任意的derive获取，获得affine仿射的dual 二元的arbitrary任意的duality二元性；二象性；对偶性amino acid氨基酸derivation求导；获取；发源amenable 经得起查验的denote预示，表示，是的标记；意味着，[逻]指称axiom 公义，原则divergence散度；发散性abstract提取dimension尺度，规格；维数architecture架构，系统构造；建筑业dot 小圆点absolute绝对的distortion变形arsenal军械库density概率密度函数assignment分派discrete失散的人工智能词汇discriminative有辨别能力的indicator指示物，指示器diagonal对角interative重复的，迭代的dispersion分别，散开integral积分determinant决定要素identical相等的；完整同样的disjoint不订交的indicate表示，指出encounter碰到invariance不变性，恒定性ellipses椭圆impose把强加于equality等式intermediate中间的extra 额外的interpretation解说，翻译empirical经验；察看joint distribution结合概率ennmerate例举，计数lieu 代替exceed超出，越出logarithmic对数的，用对数表示的expectation希望latent潜伏的efficient奏效的Leave-one-out cross validation留一法交错考证endow 给予magnitude巨大explicitly清楚的mapping 画图，制图；映照exponential family指数家族matrix矩阵equivalently等价的mutual互相的，共同的feasible可行的monotonically单一的forary首次试试minor较小的，次要的finite有限的，限制的multinomial多项的forgo 摒弃，放弃multi-class classification二分类问题fliter过滤nasty厌烦的frequentist最常发生的notation标记，说明forward search前向式搜寻na?ve 朴实的formalize使定形obtain获取generalized归纳的oscillate摇动generalization归纳，归纳；广泛化；判断（依据不optimization problem最优化问题足）objective function目标函数guarantee保证；抵押品optimal最理想的generate形成，产生orthogonal(矢量，矩阵等 ) 正交的geometric margins几何界限orientation方向gap 裂口ordinary一般的generative生产的；有生产力的occasionally有时的heuristic启迪式的；启迪法；启迪程序partial derivative偏导数hone 怀恋；磨property性质hyperplane超平面proportional成比率的initial最先的primal原始的，最先的implement履行permit同意intuitive凭直觉获知的pseudocode 伪代码incremental增添的permissible可同意的intercept截距polynomial多项式intuitious直觉preliminary预备instantiation例子precision精度人工智能词汇perturbation不安，搅乱theorem定理poist 假定，假想tangent正弦positive semi-definite半正定的unit-length vector单位向量parentheses圆括号valid 有效的，正确的posterior probability后验概率variance方差plementarity增补variable变量；变元pictorially图像的vocabulary 词汇parameterize确立的参数valued经估价的；可贵的poisson distribution柏松散布wrapper 包装pertinent有关的总计 1038 词汇quadratic二次的quantity量，数目；重量query 疑问的regularization使系统化；调整reoptimize从头优化restrict限制；限制；拘束reminiscent回想旧事的；提示的；令人联想的（ of ）remark 注意random variable随机变量respect考虑respectively各自的；分其他redundant过多的；冗余的susceptible敏感的stochastic可能的；随机的symmetric对称的sophisticated复杂的spurious假的；假造的subtract减去；减法器simultaneously同时发生地；同步地suffice知足scarce罕有的，难得的split分解，分别subset子集statistic统计量successive iteratious连续的迭代scale标度sort of有几分的squares 平方trajectory轨迹temporarily临时的terminology专用名词tolerance容忍；公差thumb翻阅threshold阈，临界。
Iterative methods for total variation denoising
DAVID C. DOBSON AND CURTIS R. VOGELy
Abstract. In total variation denoising, one attempts to remove noise from a signal or image by
may be applied. To deal with the non-di erentiability of the TV functional, interior
point methods from linear programming (see Li and Santosa 14]; note that T V (u) has
In 17], Vogel and Oman introduced a xed point iteration to minimize the penalized least squares functional
(1.6)
1 2
ku
?
zk2L2(
)
+
J (u):
The corresponding Euler-Lagrange equations
solving a nonlinear minimization problem involving a total variation criterion. Several approaches based on this idea have recently been shown to be very e ective, particularly for denoising functions with discontinuities. This paper analyzes the convergence of an iterative method for solving such problems. The iterative method involves a \lagged di usivity" approach in which a sequence of linear di usion problems are solved. Global convergence in a nite dimensional setting is established, and local convergence properties, including rates and their dependence on various parameters, are examined.
天津大学信息与通信工程考研复习辅导资料及导师分数线信息
天津大学信息与通信工程考研复习辅导资料及导师分数线信息天津大学信息与通信工程考研科目包括政治、外语、数学一以及通信原理、信号与系统。
主要研究方向分为两个，方向一考试科目为通信原理，方向二考试科目为信号与系统，此专业是报考人数较多的专业，考生需进一步把握备考方向。
考试科目备注专业代码、名称及研究方向081000信息与通信工程①101思想政治理论②201英语一③301数学一④814通信原理①101思想政治理论②201英语一③301数学一④815信号与系统天津大学信息与通信工程考研录取情况院（系、所） 专业 报考人数 录取人数信息与通信工程506 95 电子信息工程学院（2012年）信息与通信工程463 92 电子信息工程学院（2013年）天津大学信息与通信工程2012年的报考人数为506人，录取人数为95人，2013年的报考人数为463人，录取人数为92人。
由真题可以发现，现在考点涉及的广度和深度不断扩宽和加深。
由天津考研网签约的天津大学在读本硕博团队搜集整理了天津大学电子信息工程学院信息与通信工程考研全套复习资料，帮助考生梳理知识点并构建知识框架。
真题解析部分将真题按照知识点划分，条理清晰的呈现在同学们眼前。
然后根据各个考点的近几年真题解析，让同学对热点、难点了然于胸。
只有做到了对真题规律和趋势的把握，8—10月底的提高复习才能有的放矢、事半功倍！天津大学电子信息工程学院信息与通信工程考研导师信息刘开华纵向课题经费课题名称情境感知服务位置信息获取机理与算法2009-01-01--2011-12-31负责人:刘开华科技计划:国家基金委拨款单位:国家基金委合同经费:32 课题名称智能航空铅封技术研究2010-01-01--2012-12-31 负责人:刘开华科技计划:天津市科技支撑计划重点项目拨款单位:天津市科学技术委员会合同经费:50 横向课题经费课题名称基于相位法的RFID定位技术2013-01-01--2013-12-31 负责人:刘开华科技计划: 拨款单位:中兴通信有限公司合同经费:16课题名称基于ADoc芯片组的产品开发2008-09-01--2009-08-31 负责人:刘开华科技计划: 拨款单位:THOMSON宽带研发（北京）有限公司合同经费:6.3 期刊、会议论文Tan, Lingling; Bai, Yu; Teng, Jianfu; Liu, Kaihua; Meng, WenqingTrans-Impedance Filter Synthesis Based on Nodal Admittance Matrix Expansion CIRCUITS SYSTEMS AND SIGNAL PROCESSINGnullTan, Lingling; Liu, Kaihua; Bai, Yu; Teng, Jianfu Construction of CDBA and CDTA behavioral models and the applications in symbolic circuits analysis ANALOG INTEGRATED CIRCUITS AND SIGNAL PROCESSINGnullMa Yongtao，Zhou Liuji，Liu Kaihua A Subcarrier-Pair Based Resource AllocationScheme Using SensorsnullMa Yongtao，Zhou Liuji，Liu Kaihua, Wang Jinlong Iterative Phase Reconstruction and Weighted IEEE sensorsnull罗蓬，刘开华，闫格基于FrFT能量重心谱校正的LFM信号参数估计信号处理null 潘勇, 刘开华，等 A novel printed microstrip antenna with frequency reconfigurable characteristics for Bluetooth/WLAN/WiMAX applications Microwave and Optical Technology Lettersnull阎格，刘开华，吕西午基于分数阶Fourier变换的新型时频滤波器设计哈尔滨工业大学学报nullLin Zhu, Kaihua Liu, Zhang Qijun, Yongtao Ma and Bo Peng An enhanced analytical Neuro-Space Mapping method for large-signal microwave device modeling null罗蓬，刘开华，于洁潇，马永涛一种相干宽带线性调频信号的波达方向估计新方法通信学报nullLin Zhu, Yongtao Ma, Qijun Zhang and Kaihua Liu An enhanced Neuro-Space Mapping method for nonlinear device modeling nullYue Cui, Kaihua Liu, Junfeng Wang Direction-of-arrival estimation for coherent GPS signals based on oblique projection Signal ProcessingnullLV Xi-wu, LIU Kai-hua, et al. Efficient solution of additional base stations in time-of-arrival positioning systems Electronics Lettersnull省部级以上获奖刘开华；等数字电视接收系统、软件技术的研发与应用”天津市科技进步奖三等奖2011-04-29李华；刘开华；等数字视频压缩与码流测试技术的研发及应用天津市科技进步奖二等奖2009-04-29知识产权刘开华, 于洁潇高速公路上车辆的车速和相对位置实时测量系统及方法刘开华；潘勇；于洁潇；陈征一种基于无联网的车载自动实时监控远程终端刘开华，黄翔东，于洁潇，王兆华，闫格基于相位差测距的RFID无线定位方法王安国纵向课题经费课题名称基带处理与天线协同2007-07-16--2011-11-16 负责人:王安国科技计划:国家科技部拨款单位:财政部合同经费:157.41课题名称无线网络多源稀疏协作编码研究2011-01-01--2013-12-31 负责人:韩昌彩科技计划:国家基金委拨款单位:国家基金委合同经费:20横向课题经费课题名称具有波束多选择性的多频段可重构天线研究2013-01-01--2014-12-31 负责人:王安国科技计划: 拨款单位:东南大学毫米波国家重点实验室合同经费:5课题名称双方向图算法在室内定位中的应用2012-01-01--2012-12-31 负责人:冷文科技计划: 拨款单位:中兴通讯股份有限公司合同经费:14.5期刊、会议论文马宁王安国姬雨初石和平Cooperative Space Shift Keying for Multiple-Relay Network IEEE Communications Lettersnull裴静王安国高顺,冷文Miniaturized Triple-Band Antenna With a Defected Ground Plane for WLAN/WiMAX Applications IEEE Antennas and Wireless Propagation Lettersnull赵国煌王安国冷文陈彬陈华Wideband internal antenna with coupled feeding for 4G mobile phone Microwave and Optical Technology Lettersnull陈彬王安国赵国煌Design of a novel ultrawideband antenna with dualband-notched characteristics Microwave and Optical technology lettersnull 蔡晓涛王安国马宁冷文 A Novel Planar Parasitic Array Antenna with Reconfigurable Azimuth pattern IEEE Antennas and Wireless Propagation Lettersnull 马宁王安国聂仲尔曲倩倩姬雨初Adaptive Mapping Generalized Space Shift Keying Modulation China Communicationsnull王安国蔡晓涛冷文带寄生贴片的圆盘形方向图可重构天线设计电波科学学报null 王安国陈彬冷文赵国煌一种小型化五频段可重构蝶形天线的设计电波科学学报null蔡晓涛王安国马宁冷文Novel radiation pattern reconfigurable antenna with six beam choices The Journal of China Universities of Posts and Telecommunicationsnull 曲倩倩王安国聂仲尔郑剑锋Block Mapping Spatial Modulation Scheme forMIMO Systems The Journal of China Universities of Posts and Telecommunicationsnull王安国刘楠兰航方向图可重构宽带准八木天线的设计天津大学学报null李锵纵向课题经费课题名称基于稀疏核支持向量机的音乐自动分类系统关键技术研究2009-06-01--2010-06-01 负责人:李锵科技计划: 拨款单位:天津大学建筑设计研究院合同经费:3课题名称jg预研项目2010-03-01--2010-12-01 负责人:李锵科技计划:拨款单位:渤海石油运输有限责任公司合同经费:3课题名称超声波热治疗中非侵入式温度成像与弹性成像关键技术研究2015-01-01--2018-12-31 负责人:李锵科技计划:国家自然科学基金项目拨款单位: 国家自然科学基金委员会合同经费:85课题名称高等学校学科创新引智计划综合管理平台的设计与开发2010-04-01--2012-04-01 负责人:李锵科技计划: 拨款单位:苏州国芯科技有限公司合同经费:3横向课题经费课题名称微粒捕集器数据采集系统开发2008-01-01--2008-06-01 负责人:李锵科技计划: 拨款单位:润英联新加坡私人有限公司合同经费:22.5课题名称电子系统可靠性增长建模与仿真2006-12-01--2008-01-01 负责人:李锵科技计划: 拨款单位:中国人民解放军海军航空工程学院合同经费:5期刊、会议论文李锵，滕建辅，赵全明，李士心Wavelet domain Wiener filter and its application in signal denoising null张立毅，李锵，刘婷，滕建辅The research of the adaptive blind equalizer's steady residual error null徐星，李锵，关欣Chinese folk instruments classification via statistical features and sparse-based representation null张立毅，李锵，刘婷，滕建辅Study of improved constant modulus blind equalization algorithm null张立毅，孙云山，李锵，滕建辅Study on the fuzzy neural network classifier blind equalization algorithm null郭继昌，滕建辅，李锵Research of the gyro signal de-noising method based on stationary wavelets transform null肖志涛，于明，李锵，国澄明Symmetry phase congruency: Feature detector consistent with human visual system characteristics nullCai wei，李锵，关欣Automatic singer identification based on auditory features. null李锵，滕建辅，王昕，张雅绮，郭继昌Research of gyro signal de-noising with stationary wavelets transform null郭继昌，滕建辅，李锵，张雅绮The de-noising of gyro signals by bi-orthogonalwavelet transform nullLiu Tianlong，李锵，关欣Double boundary periodic extension DNA coding sequence detection algorithm combining base content null关欣，滕建辅，李锵，苏育挺Blind acoustic source separation combiningtime-delayed autocorrelation and 4TH-order cumulants null张立毅，李锵，滕建辅Kurtosis-driven variable step size blind equalization algorithm with constant module nullQin Lu，李锵，关欣Pitch Extraction for Musical Signals with Modified AMDF null Zhang Xueying，李锵，关欣The Improved AMDF Gene Exon Prediction null 李锵，Jian Dong,Ming-Guo Wang,滕建辅Analysis and simulation of antenna protocol optimization for ad hoc networks nullFeng Yanyan，李锵，关欣Entropy of Teager Energy in Wavelet-domain Algorithm Applied in Note Onset Detection nullBao Hu, Li ShangSheng, 李锵，滕建辅Research on the technology of RFSS in large-scale universal missile ATE null张立毅，Haiqing Cheng,李锵，滕建辅 A research of forward neural network blind equalization algorithm based on momentum term null张立毅，李锵，滕建辅 A New Adaptive Variable Step-size Blind Equalization Algorithm Based on Forward Neural Network nullYutao Ma,李锵，Chao Li，Kun Li，滕建辅Design of active transimpedanceband-pass filters with different Q values International Journal of Electronicsnull 夏静静，李锵,刘浩澧，Wen-shiang Chen,Po-Hsiang Tsui An Approach for the Visualization of Temperature Distribution in Tissues According to Changes in Ultrasonic Backscattered Computational and Mathematical Methods in Medicinenull 耿晓楠，李锵，崔博翔，王荞茵，刘浩澧超声温度影像与弹性成像监控组织射频消融南方医科大学学报null谭玲玲, 李锵, 李瑞杰, 滕建辅Design of transimpedance low-pass filters International Journal of Electronicsnull李锵，李秋颖，关欣基于听觉图像的音乐流派自动分类天津大学学报（自然科学与工程技术版）nullChong Zhou, Wei Pang, 李锵, Hongyu Yu, Xiaotang Hu, HaoZhang, Extracting the Electromechanical Coupling Constant of Piezoelectric Thin Film by the High-Tone Bulk Acoustic Resonator IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Controlnull朱琳, 李锵, 刘开华基于ADS的声表面波单端对谐振器建模压电与声光null董丽梦, 李锵, 关欣基于稀疏表示分类器的音乐和弦识别系统研究计算机工程与应用null关欣，李锵，田洪伟基于差分全相位MFCC的音符起点自动检测计算机工程null 关欣，李锵，郭继昌，滕建辅二、四阶组合时延统计量多乐器盲分离计算机工程与应用null杨甲沛, 李锵, 刘郑, 袁晓琳基于自适应学习速率的改进型BP算法研究计算机工程与应用null李锵, 张法朝, 张瑞峰System design of DPF data recorder and data analysisnull李锵, 袁晓琳, 杨甲沛Application of ant colony algorithm in the optimization of the time environmental conversion factor of the reliability models null 张立毅，白煜，李锵，滕建辅复数系统中五二阶归一化积累盲均衡算法的研究通信学报null郭继昌，关欣，李锵，刘志杨红外图像预处理系统中模拟视频输出时序设计电子技术应用null关欣，滕建辅，李锵，苏育挺，Wang Shu-Yan Blind source separation combining time-delayed second and fourth order statistics 天津大学学报（自然科学与工程技术版）null张立毅，李锵，滕建辅复数系统中三、二阶归一化累积量盲均衡算法的研究计算机工程与应用null张立毅，李锵，滕建辅经典盲均衡算法中稳态剩余误差的分析天津大学学报null 滕建辅，董健，李锵，关欣Design of maximally flat FIR filters based on explicit formulas combined with optimization 天津大学学报（英文版）null郭继昌，陈敏俊，李锵，关欣红外焦平面失效元处理方法及软硬件实现光电工程null 马杰，王昕，李锵，滕建辅基于特征值和奇异值分解方法的盲分离天津大学学报（自然科学与工程技术版）null李锵，郭继昌，关欣，滕建辅基于通用DSP的红外焦平面视频图像数字预处理系统天津大学学报（自然科学与工程技术版）null李锵，郭继昌，关欣，刘航，童央群基于DSP的红外焦平面视频图像数字处理系统的设计测控技术null马杰，滕建辅，李锵具有参考噪声源的多路传感器信号盲分离方法测控技术null 周郭飞，李锵，滕建辅微带扇形分支线在低通滤波器设计中应用电子测量技术null 李锵，滕建辅，李士心，肖志涛小波域Wiener滤波器信号的去噪方法天津大学学报（自然科学与工程技术版）null肖志涛，于明，李锵，唐红梅，国澄明Log Gabor小波性能分析及其在相位一致性中应用天津大学学报（自然科学与工程技术版）null罗批，李锵，郭继昌，滕建辅Improved genetic algorithm and its performance analysis 天津大学学报（英文版）null罗批，郭继昌，李锵，滕建辅一种实用的电子线路参数优化算法电路与系统学报null 罗批，李锵，郭继昌，滕建辅基于偏最小二乘回归建模的探讨天津大学学报null 知识产权李锵，闫志勇，关欣一种结合SVM和增强型PCP特征的和弦识别方法中国2014100089231李锵, 冯亚楠, 关欣基于Teager能量熵的音符切分方法学术专著(关欣, 杨爱萍, 白煜, 李锵), 信号检测与估计:理论与应用（译著）, 电子工业出版社2012-01-31(白煜, 李锵), 模拟集成电路设计的艺术(译著), 人民邮电出版社2010-11-04(李锵，周进等), 无线通信基础（译著）, 人民邮电出版社2007-06-30(李锵，董健，关欣，鲍虎), 数字通信（原书第2版）（译著）, 机械工业出版社2006-02-28(张为，关欣，刘艳艳，李锵), 电子电路设计基础（译著）, 电子工业出版社2005-10-01(张雅绮，李锵等), Verilog HDL高级数字设计（译著）, 电子工业出版社2005-01-31(李锵，侯春萍，赵宇), 网络（原书第2版）（译著）, 机械工业出版社2004-11-30(李锵，郭继昌), 无线通信与网络, 电子工业出版社2004-06-30本文内容摘自《天津大学814通信原理考研红宝书》，更多考研资料可登陆网站下载！。
单轴光纤陀螺寻北仪
用精密蜗轮、蜗杆副传动，设计传动比 160∶1。通过提高蜗轮蜗杆副的加工精度和装配精度，提高了转台的定位精度。
1.2.2 精密转位控制系统 转位控制系统由微处理器、步进电机及其驱动电路、光电编码器及其解码电路构成。系统采用分辨率为 1"的绝对式
第2期
蒋庆仙等：单轴光纤陀螺寻北仪的研究
167
光电编码器作为步进电机位置控制的反馈机构，构成闭环控制系统。采用 8 位单片机作为控制器实现数字控制，对步进 电机的定位位置进行修正。采用了分体式光电编码器，将码盘、轴系和底盘进行了一体化设计。码盘直接与内轴固连，
（1）
α
=
2
cos γ arctan(
−
cos2 γ + sin2 β sin2 γ − A2
A + sin β sin γ
（2）
式中， A = ω1x
− ω2x
− εr1 + εr2 + 2ωe sinφ cos β sin γ 2ωe cosφ
， εr 为陀螺随机漂
移，包含周期噪声和白噪声等各种随机干扰信号。
收稿日期：2009-11-05；修回日期：2010-03-02 作者简介：蒋庆仙（1969—），女，高级工程师，主要从事陀螺定向技术的研究。 E-mail：jiangqingx@
166
中国惯性技术学报
第 18 卷
一固定轴与真北方向的夹角[4]。为了实现 360°全方位寻北，增加了从初始位置逆时针旋转 90°的采样位置（采样数秒钟）， 由解算出的方位角的符号和 90°位置陀螺输出值的符号可唯一地确定出陀螺敏感轴的真北方位角。由于陀螺仪和加速度计
文章编号：1005-6734(2010)02-0165-05
自动控制原理 附录B 控制理论术语中英文对照表
附录B 控制理论术语中英文对照表AAbsolute error 绝对误差Absolute value 绝对值Accuracy 精确度Activate 启动，触发Active electric network 有源网络Actuating signal 作用信号，启动信号Actuator 执行机构，调节器，激励器Adjust 调整Adaptive control 自适应控制Algebraic operations 代数运算Amplifier 放大器Amplitude 振幅，幅值Analog computer 模拟计算机Analog signal 模拟信号Angle condition 相角条件Angle of arrival 入射角Angle of departure 出射角Angular acceleration 角加速度Argument 幅角Armature 电枢Asymptote 渐近线Asymptotic stable 渐近稳定的Automatic control 自动控制Attenuation 衰减Auxiliary equation 辅助方程BBacklash 间隙，回差Bandwidth 带宽Bang-bang control 砰－砰控制，继电控制Be proportional to 与……成比例自动控制原理·326· ·326·Biocybernetics 生物控制论Block diangram 方框图，方块图，结构图 Bode plot 波特图 Branch分支，支路 Breakaway points 分离点 Bump 撞击，扰动 By-pass旁路CCACSD(Computer-Aided Control System Design) 控制系统计算机辅助设计 CACSE(Computer-Aided Control System Engineering) 控制系统计算机辅助工程 CAD(computer aided design) 计算机辅助设计 Cascade compensation 串联补偿校正 Cascade control 串级控制 Channel通道 Characteristic equation 特征方程 Characteristic gain locus 特征增益轨迹 Circuit电路Classical control theory 古典控制理论 Closed loop control system 闭环控制系统 Closed loop frequency response 闭环频率响应 Closed loop pole 闭环极点 Closed loop zero闭环零点 Combinational control system 复合控制系统 Comparator 比较器Comparing element 比较元件，比较环节 Compound control 复合控制 Compensation 补偿，校正 Complex plane 复平面 Conditional stability 条件隐定Configuration 结构，配置，方案，组态 Constant M loci 等M 圆 Continuous system 连续系统 Controlled variable 被控变量 Controlling machine 控制机 Control system 控制系统 Control valve 调节阀 Controllability 可控性，能控性附录B控制理论术语中英文对照表·327·Conveyor 传送器，传送带，传送装置Corner frequency 转折频率，交接频率Correcting unit 执行器Correction 校正Coupling 耦合Criterion 判据，准则Critical damping 临界阻尼Cut off rate 剪切率Cut off frequency 剪切频率Cybernetics 控制论DDamped natural frequency 有阻尼自然频率Dampe r 阻尼器Damping factor 阻尼系数Damping ratio 阻尼比Dead band 死区Dead time 纯延迟，延迟时间Decay 衰减，衰变Decomposition 分解Delay 滞后Delay element 滞后环节Denominator 分母Derivation action 微分作用Derivative control 微分控制Desired value 预期值，期望值Determinant 行列式Deviation 偏差Differencing junction 比较点Differential equations 微分方程Digital computer 数字计算机Discrete-data system 离散数据系统Disturbance 扰动，干扰Disturbance rejection property 抗干扰特性Dominate 主导Duality 对偶性Dynamic equation 动态方程Dynamic error 动态误差·327·自动控制原理·328··328·Dynamic process动态过程EEquilibrium state 平衡状态 Eigenvalue 特征值 Eigenvector 特征向量 Element 元件，环节 Error误差 Error coefficient 误差系数 Error signal 误差信号 Even symmetry 偶对称Exponential 指数，指数的，幂的 External description 外部描述 Extremum极值FFeasibility 可行性，可能性，现实性 Feedback 反馈 Feedback control 反馈控制 Feedback element 反馈环节 Feedback path 反馈通道 Feedforward前馈 Final controlling element 执行器 Final value 终值 First-order system 一阶系统 Focus焦点 Following device 随动装置 Forward path 前向通道 Fraction 分数 Frequency 频率 Frequency domain 频域 Frequency response频率响应 Frequency response characteristic 频率响应特性 Function 函数 Fuzzy control模糊控制附录B控制理论术语中英文对照表·329·GGain 增益Gain margin 增益裕度，幅值裕度Gear backlash 齿轮间隙General solution 通解Graphical method 图解法Guidance system 制导系统Gravitation area 引力域Gyro 陀螺HHarmonic 谐波，谐波量，谐振荡Harmonic response 谐波响应Holder 保持器Homogeneous equation 齐次方程Hurwitz determinant 赫尔维茨行列式Hydraulic system 液压系统Hysteresis error 回差Hysteresis loop 磁滞回环IIdealized system 理想化系统Identification 辨识Impulse response 脉冲响应Industrial robot 工业机器人Inertial 惯性的，惯量的，惰性的Inherent characteristic 固有特性Initial condition 初始条件Initial state 初始状态Initial value theorem 初值定理Inner loop 内环Input 输入Input node 输入节点Input signal 输入信号·329·自动控制原理·330· ·330·Integral action 积分作用 Integral control积分控制 IAE(integrated absolute error) 绝对误差积分 ISE(integrated square error) 平方误差积分 Internal description 内部描述 Intelligent instrument 智能仪表 Invariant 不变的，恒定的 Inverse matrix 逆矩阵 Inverse transformation 反变换 Inverse Laplace transforms 拉普拉斯反变换 Isocline method 等倾线法 Iterative algorithm迭代算法JJordan block 约旦块 Jordan canonical form约旦标准型KKalman criterion 卡尔曼准则 Kalman filter卡尔曼滤波LLag network 滞后网络 Lag compensation 滞后补偿 Laplace transforms 拉普拉斯变换 Large scale system 大系统 Lead network 超前网络 Least-mean-square 最小均方 Limit cycle 极限环 Linearization 线性化 Linearity 线性度 Linear equation 线性方程 Linear system 线性系统 Linear programming 线性规划 Load 负载附录B控制理论术语中英文对照表·331·Load-response curve 负荷响应曲线Locus 轨迹Logic diagram 逻辑图Log magnitude 对数幅值Low pass characteristic 低通特性MMagnitude condition 幅值条件Magnitude-versus-phase plot 幅相特性曲线Manipulated variable 操纵变量Mason rule 梅逊公式Mathematical model 数学模型Matrix 矩阵Maximum overshoot 最大超调量Measurable 可测量的Measured variable 被测变量Minimum phase system 最小相位系统Model decomposition 模型分解Modulus 模Moment of inertia 转动惯量Multinomial 多项式（的）Multivariable system 多变量系统NNatural frequency 自然频率Negative feedback 负反馈Nichols chart 尼柯尔斯图线Node 节点Noise 噪声Nonlinear control system 非线性控制系统Nonminimum phase system 非最小相位系统Nonsingular 非奇异的Norm 范数Numerator 分子Numerical control 数字控制，数控Nyquist criterion 奈奎斯特判据Nyquist contour 奈奎斯特轨线·331·自动控制原理·332··332·OObjective function 目标函数Observability 可观性，能观性Observer 观测器Odd symmetry 奇对称Off line 离线Offset 偏移，位移On line 在线Open loop 开环Optimal control 最优控制Optimization 最优化Origin 原点Oscillating loop 振荡回路Oscillation 振荡Oscillatory response 振荡响应Outer loop 外环Output 输出Output signal 输出信号Over damping 过阻尼Overshoot 超调量PParameter 参数Peak overshoot 超调峰值Peak time 峰值时间Performance index 性能指标Perturbance 扰动，摄动Phase lag 相位滞后Phase lead 相位超前Phase margin 相角裕度Phase modifier 相位调节器Phase plane 相平面Pickoff point 引出点PID(proportional plus integral plus derivative) controller PID (比例、积分、微分)控制器Piece-wise linearization 分段线性化Pneumatic controller 气动调节器，气动控制器附录B控制理论术语中英文对照表·333·Pole 极点Pole assignment 极点配置Polynomial 多项式Position error 位置误差Positive definiteness 正定性Pre-compensator 预补偿器Process control 过程控制Proportional action 比例作用Proportional band 比例带Proportional control 比例控制Prototype 原型，模型，样机Pulse 脉冲Pulse width 脉宽Pure delay 纯滞后QQuadratic 二次的Quadratic form 二次型Quality control 质量控制Quantizer 数字转换器RRamp input 斜坡输入Ramp response 斜坡响应Rate feedback 速度反馈Rate time 微分时间，预调时间Rational 有理（数）的，合理的Rational number 有理数Realization 实现Reference variable 参考变量Regulator 调节器Relay 继电器Relative stability 相对稳定性Reliability 可靠性Remote control 遥控Reproducibility 再现性Resilience 弹性，弹性形变·333·自动控制原理·334· ·334·Resonance 谐振 Response 响应Reset time 再调时间，积分时间 Residue 留数 Rise time上升时间 RMS(root mean square) 均方根 Roots loci 根轨迹 Routh array劳斯阵列Routh-Hurwitz criterion 劳斯-赫尔维茨判据 Routh stability criterion劳斯稳定判据SSampling control 采样控制 Sampling frequency 采样频率 Sampling period 采样周期 Saturation 饱和 Scalar function 标量函数 Scaling factor 比例因子 Sensitivity 灵敏度 Sensor传感器 Series compensation 串联补偿Servo 伺服机构，伺服电机 Servodrive 伺服传动，伺服传动装置 Set point 设定点 Set value 设定值Settling time 调节时间；稳定时间 Signal flow graph 信号流图 Singularity 奇点 Sinusoidal 正弦的 Slope 斜率 Stability 稳定（性） Stability margin 稳定裕度 State equations 状态方程 State space 状态空间 State variables 状态变量 Stationary 稳态的 Steady-state 稳态 Steady-state deviation 稳态偏差附录B控制理论术语中英文对照表·335·Steady-state error 稳态误差Step singal 阶跃信号Step response 阶跃响应Stochastic process 随机过程Summing junction 相加点Superposition 叠加Supervise 监控，检测，操纵System 系统Systematic deviation 系统偏差System identification系统辨识TTangent 切线Terminology 术语Threshold value 阈值Time constant 时间常数Time domain 时域Time response时间响应Time-invariant system 常定（时不变）系统Time-varying system 时变系统Trajectory 轨迹Transducer 传感器，变换器Transfer function 传递函数Transfer matrix转移矩阵Transient response 暂态响应Transmitter 变送器Transportation lag 传输滞后Transpose 转置（阵）UUndamped natural frequency 无阻尼自然频率Underdamping欠阻尼Uniform stability 一致稳定Unit circle 单位圆Unit impulse 单位脉冲Unit step function 单位阶跃函数Unit feedback 单位反馈·335·自动控制原理·336· ·336·Unit matrix 单位矩阵 Unstable 不稳定的 Unsymmetrical不对称的VValue of quantity 量值 Variable 变量 Vector向量 Velocity feedback 速度反馈 Viscous friction黏摩擦WWave 波 Waveform 波形 Weighting function 加权函数 White noise白噪声ZZero零点 Zero input response 零输入响应 Zero-order holder 零阶保持器 Zero-state response 零状态响应 Z-transfer function z 传递函数 Z-transformationz 变换。
机器学习英语词汇
目录第一部分 (3)第二部分 (12)Letter A (12)Letter B (14)Letter C (15)Letter D (17)Letter E (19)Letter F (20)Letter G (21)Letter H (22)Letter I (23)Letter K (24)Letter L (24)Letter M (26)Letter N (27)Letter O (29)Letter P (29)Letter R (31)Letter S (32)Letter T (35)Letter U (36)Letter W (37)Letter Z (37)第三部分 (37)A (37)B (38)C (38)D (40)E (40)F (41)G (41)H (42)L (42)J (43)L (43)M (43)N (44)O (44)P (44)Q (45)R (46)S (46)U (47)V (48)第一部分[ ] intensity 强度[ ] Regression 回归[ ] Loss function 损失函数[ ] non-convex 非凸函数[ ] neural network 神经网络[ ] supervised learning 监督学习[ ] regression problem 回归问题处理的是连续的问题[ ] classification problem 分类问题处理的问题是离散的而不是连续的回归问题和分类问题的区别应该在于回归问题的结果是连续的，分类问题的结果是离散的。
[ ]discreet value 离散值[ ] support vector machines 支持向量机,用来处理分类算法中输入的维度不单一的情况（甚至输入维度为无穷）[ ] learning theory 学习理论[ ] learning algorithms 学习算法[ ] unsupervised learning 无监督学习[ ] gradient descent 梯度下降[ ] linear regression 线性回归[ ] Neural Network 神经网络[ ] gradient descent 梯度下降监督学习的一种算法，用来拟合的算法[ ] normal equations[ ] linear algebra 线性代数原谅我英语不太好[ ] superscript上标[ ] exponentiation 指数[ ] training set 训练集合[ ] training example 训练样本[ ] hypothesis 假设，用来表示学习算法的输出，叫我们不要太纠结H的意思，因为这只是历史的惯例[ ] LMS algorithm “least mean squares” 最小二乘法算法[ ] batch gradient descent 批量梯度下降，因为每次都会计算最小拟合的方差，所以运算慢[ ] constantly gradient descent 字幕组翻译成“随机梯度下降” 我怎么觉得是“常量梯度下降”也就是梯度下降的运算次数不变，一般比批量梯度下降速度快，但是通常不是那么准确[ ] iterative algorithm 迭代算法[ ] partial derivative 偏导数[ ] contour 等高线[ ] quadratic function 二元函数[ ] locally weighted regression局部加权回归[ ] underfitting欠拟合[ ] overfitting 过拟合[ ] non-parametric learning algorithms 无参数学习算法[ ] parametric learning algorithm 参数学习算法[ ] other[ ] activation 激活值[ ] activation function 激活函数[ ] additive noise 加性噪声[ ] autoencoder 自编码器[ ] Autoencoders 自编码算法[ ] average firing rate 平均激活率[ ] average sum-of-squares error 均方差[ ] backpropagation 后向传播[ ] basis 基[ ] basis feature vectors 特征基向量[50 ] batch gradient ascent 批量梯度上升法[ ] Bayesian regularization method 贝叶斯规则化方法[ ] Bernoulli random variable 伯努利随机变量[ ] bias term 偏置项[ ] binary classfication 二元分类[ ] class labels 类型标记[ ] concatenation 级联[ ] conjugate gradient 共轭梯度[ ] contiguous groups 联通区域[ ] convex optimization software 凸优化软件[ ] convolution 卷积[ ] cost function 代价函数[ ] covariance matrix 协方差矩阵[ ] DC component 直流分量[ ] decorrelation 去相关[ ] degeneracy 退化[ ] demensionality reduction 降维[ ] derivative 导函数[ ] diagonal 对角线[ ] diffusion of gradients 梯度的弥散[ ] eigenvalue 特征值[ ] eigenvector 特征向量[ ] error term 残差[ ] feature matrix 特征矩阵[ ] feature standardization 特征标准化[ ] feedforward architectures 前馈结构算法[ ] feedforward neural network 前馈神经网络[ ] feedforward pass 前馈传导[ ] fine-tuned 微调[ ] first-order feature 一阶特征[ ] forward pass 前向传导[ ] forward propagation 前向传播[ ] Gaussian prior 高斯先验概率[ ] generative model 生成模型[ ] gradient descent 梯度下降[ ] Greedy layer-wise training 逐层贪婪训练方法[ ] grouping matrix 分组矩阵[ ] Hadamard product 阿达马乘积[ ] Hessian matrix Hessian 矩阵[ ] hidden layer 隐含层[ ] hidden units 隐藏神经元[ ] Hierarchical grouping 层次型分组[ ] higher-order features 更高阶特征[ ] highly non-convex optimization problem 高度非凸的优化问题[ ] histogram 直方图[ ] hyperbolic tangent 双曲正切函数[ ] hypothesis 估值，假设[ ] identity activation function 恒等激励函数[ ] IID 独立同分布[ ] illumination 照明[100 ] inactive 抑制[ ] independent component analysis 独立成份分析[ ] input domains 输入域[ ] input layer 输入层[ ] intensity 亮度/灰度[ ] intercept term 截距[ ] KL divergence 相对熵[ ] KL divergence KL分散度[ ] k-Means K-均值[ ] learning rate 学习速率[ ] least squares 最小二乘法[ ] linear correspondence 线性响应[ ] linear superposition 线性叠加[ ] line-search algorithm 线搜索算法[ ] local mean subtraction 局部均值消减[ ] local optima 局部最优解[ ] logistic regression 逻辑回归[ ] loss function 损失函数[ ] low-pass filtering 低通滤波[ ] magnitude 幅值[ ] MAP 极大后验估计[ ] maximum likelihood estimation 极大似然估计[ ] mean 平均值[ ] MFCC Mel 倒频系数[ ] multi-class classification 多元分类[ ] neural networks 神经网络[ ] neuron 神经元[ ] Newton’s method 牛顿法[ ] non-convex function 非凸函数[ ] non-linear feature 非线性特征[ ] norm 范式[ ] norm bounded 有界范数[ ] norm constrained 范数约束[ ] normalization 归一化[ ] numerical roundoff errors 数值舍入误差[ ] numerically checking 数值检验[ ] numerically reliable 数值计算上稳定[ ] object detection 物体检测[ ] objective function 目标函数[ ] off-by-one error 缺位错误[ ] orthogonalization 正交化[ ] output layer 输出层[ ] overall cost function 总体代价函数[ ] over-complete basis 超完备基[ ] over-fitting 过拟合[ ] parts of objects 目标的部件[ ] part-whole decompostion 部分-整体分解[ ] PCA 主元分析[ ] penalty term 惩罚因子[ ] per-example mean subtraction 逐样本均值消减[150 ] pooling 池化[ ] pretrain 预训练[ ] principal components analysis 主成份分析[ ] quadratic constraints 二次约束[ ] RBMs 受限Boltzman机[ ] reconstruction based models 基于重构的模型[ ] reconstruction cost 重建代价[ ] reconstruction term 重构项[ ] redundant 冗余[ ] reflection matrix 反射矩阵[ ] regularization 正则化[ ] regularization term 正则化项[ ] rescaling 缩放[ ] robust 鲁棒性[ ] run 行程[ ] second-order feature 二阶特征[ ] sigmoid activation function S型激励函数[ ] significant digits 有效数字[ ] singular value 奇异值[ ] singular vector 奇异向量[ ] smoothed L1 penalty 平滑的L1范数惩罚[ ] Smoothed topographic L1 sparsity penalty 平滑地形L1稀疏惩罚函数[ ] smoothing 平滑[ ] Softmax Regresson Softmax回归[ ] sorted in decreasing order 降序排列[ ] source features 源特征[ ] sparse autoencoder 消减归一化[ ] Sparsity 稀疏性[ ] sparsity parameter 稀疏性参数[ ] sparsity penalty 稀疏惩罚[ ] square function 平方函数[ ] squared-error 方差[ ] stationary 平稳性（不变性）[ ] stationary stochastic process 平稳随机过程[ ] step-size 步长值[ ] supervised learning 监督学习[ ] symmetric positive semi-definite matrix 对称半正定矩阵[ ] symmetry breaking 对称失效[ ] tanh function 双曲正切函数[ ] the average activation 平均活跃度[ ] the derivative checking method 梯度验证方法[ ] the empirical distribution 经验分布函数[ ] the energy function 能量函数[ ] the Lagrange dual 拉格朗日对偶函数[ ] the log likelihood 对数似然函数[ ] the pixel intensity value 像素灰度值[ ] the rate of convergence 收敛速度[ ] topographic cost term 拓扑代价项[ ] topographic ordered 拓扑秩序[ ] transformation 变换[200 ] translation invariant 平移不变性[ ] trivial answer 平凡解[ ] under-complete basis 不完备基[ ] unrolling 组合扩展[ ] unsupervised learning 无监督学习[ ] variance 方差[ ] vecotrized implementation 向量化实现[ ] vectorization 矢量化[ ] visual cortex 视觉皮层[ ] weight decay 权重衰减[ ] weighted average 加权平均值[ ] whitening 白化[ ] zero-mean 均值为零第二部分Letter A[ ] Accumulated error backpropagation 累积误差逆传播[ ] Activation Function 激活函数[ ] Adaptive Resonance Theory/ART 自适应谐振理论[ ] Addictive model 加性学习[ ] Adversarial Networks 对抗网络[ ] Affine Layer 仿射层[ ] Affinity matrix 亲和矩阵[ ] Agent 代理/ 智能体[ ] Algorithm 算法[ ] Alpha-beta pruning α-β剪枝[ ] Anomaly detection 异常检测[ ] Approximation 近似[ ] Area Under ROC Curve／AUC Roc 曲线下面积[ ] Artificial General Intelligence/AGI 通用人工智能[ ] Artificial Intelligence/AI 人工智能[ ] Association analysis 关联分析[ ] Attention mechanism 注意力机制[ ] Attribute conditional independence assumption 属性条件独立性假设[ ] Attribute space 属性空间[ ] Attribute value 属性值[ ] Autoencoder 自编码器[ ] Automatic speech recognition 自动语音识别[ ] Automatic summarization 自动摘要[ ] Average gradient 平均梯度[ ] Average-Pooling 平均池化Letter B[ ] Backpropagation Through Time 通过时间的反向传播[ ] Backpropagation/BP 反向传播[ ] Base learner 基学习器[ ] Base learning algorithm 基学习算法[ ] Batch Normalization/BN 批量归一化[ ] Bayes decision rule 贝叶斯判定准则[250 ] Bayes Model Averaging／BMA 贝叶斯模型平均[ ] Bayes optimal classifier 贝叶斯最优分类器[ ] Bayesian decision theory 贝叶斯决策论[ ] Bayesian network 贝叶斯网络[ ] Between-class scatter matrix 类间散度矩阵[ ] Bias 偏置/ 偏差[ ] Bias-variance decomposition 偏差-方差分解[ ] Bias-Variance Dilemma 偏差–方差困境[ ] Bi-directional Long-Short Term Memory/Bi-LSTM 双向长短期记忆[ ] Binary classification 二分类[ ] Binomial test 二项检验[ ] Bi-partition 二分法[ ] Boltzmann machine 玻尔兹曼机[ ] Bootstrap sampling 自助采样法／可重复采样／有放回采样[ ] Bootstrapping 自助法[ ] Break-Event Point／BEP 平衡点Letter C[ ] Calibration 校准[ ] Cascade-Correlation 级联相关[ ] Categorical attribute 离散属性[ ] Class-conditional probability 类条件概率[ ] Classification and regression tree/CART 分类与回归树[ ] Classifier 分类器[ ] Class-imbalance 类别不平衡[ ] Closed -form 闭式[ ] Cluster 簇/类/集群[ ] Cluster analysis 聚类分析[ ] Clustering 聚类[ ] Clustering ensemble 聚类集成[ ] Co-adapting 共适应[ ] Coding matrix 编码矩阵[ ] COLT 国际学习理论会议[ ] Committee-based learning 基于委员会的学习[ ] Competitive learning 竞争型学习[ ] Component learner 组件学习器[ ] Comprehensibility 可解释性[ ] Computation Cost 计算成本[ ] Computational Linguistics 计算语言学[ ] Computer vision 计算机视觉[ ] Concept drift 概念漂移[ ] Concept Learning System /CLS 概念学习系统[ ] Conditional entropy 条件熵[ ] Conditional mutual information 条件互信息[ ] Conditional Probability Table／CPT 条件概率表[ ] Conditional random field/CRF 条件随机场[ ] Conditional risk 条件风险[ ] Confidence 置信度[ ] Confusion matrix 混淆矩阵[300 ] Connection weight 连接权[ ] Connectionism 连结主义[ ] Consistency 一致性／相合性[ ] Contingency table 列联表[ ] Continuous attribute 连续属性[ ] Convergence 收敛[ ] Conversational agent 会话智能体[ ] Convex quadratic programming 凸二次规划[ ] Convexity 凸性[ ] Convolutional neural network/CNN 卷积神经网络[ ] Co-occurrence 同现[ ] Correlation coefficient 相关系数[ ] Cosine similarity 余弦相似度[ ] Cost curve 成本曲线[ ] Cost Function 成本函数[ ] Cost matrix 成本矩阵[ ] Cost-sensitive 成本敏感[ ] Cross entropy 交叉熵[ ] Cross validation 交叉验证[ ] Crowdsourcing 众包[ ] Curse of dimensionality 维数灾难[ ] Cut point 截断点[ ] Cutting plane algorithm 割平面法Letter D[ ] Data mining 数据挖掘[ ] Data set 数据集[ ] Decision Boundary 决策边界[ ] Decision stump 决策树桩[ ] Decision tree 决策树／判定树[ ] Deduction 演绎[ ] Deep Belief Network 深度信念网络[ ] Deep Convolutional Generative Adversarial Network/DCGAN 深度卷积生成对抗网络[ ] Deep learning 深度学习[ ] Deep neural network/DNN 深度神经网络[ ] Deep Q-Learning 深度Q 学习[ ] Deep Q-Network 深度Q 网络[ ] Density estimation 密度估计[ ] Density-based clustering 密度聚类[ ] Differentiable neural computer 可微分神经计算机[ ] Dimensionality reduction algorithm 降维算法[ ] Directed edge 有向边[ ] Disagreement measure 不合度量[ ] Discriminative model 判别模型[ ] Discriminator 判别器[ ] Distance measure 距离度量[ ] Distance metric learning 距离度量学习[ ] Distribution 分布[ ] Divergence 散度[350 ] Diversity measure 多样性度量／差异性度量[ ] Domain adaption 领域自适应[ ] Downsampling 下采样[ ] D-separation （Directed separation）有向分离[ ] Dual problem 对偶问题[ ] Dummy node 哑结点[ ] Dynamic Fusion 动态融合[ ] Dynamic programming 动态规划Letter E[ ] Eigenvalue decomposition 特征值分解[ ] Embedding 嵌入[ ] Emotional analysis 情绪分析[ ] Empirical conditional entropy 经验条件熵[ ] Empirical entropy 经验熵[ ] Empirical error 经验误差[ ] Empirical risk 经验风险[ ] End-to-End 端到端[ ] Energy-based model 基于能量的模型[ ] Ensemble learning 集成学习[ ] Ensemble pruning 集成修剪[ ] Error Correcting Output Codes／ECOC 纠错输出码[ ] Error rate 错误率[ ] Error-ambiguity decomposition 误差-分歧分解[ ] Euclidean distance 欧氏距离[ ] Evolutionary computation 演化计算[ ] Expectation-Maximization 期望最大化[ ] Expected loss 期望损失[ ] Exploding Gradient Problem 梯度爆炸问题[ ] Exponential loss function 指数损失函数[ ] Extreme Learning Machine/ELM 超限学习机Letter F[ ] Factorization 因子分解[ ] False negative 假负类[ ] False positive 假正类[ ] False Positive Rate/FPR 假正例率[ ] Feature engineering 特征工程[ ] Feature selection 特征选择[ ] Feature vector 特征向量[ ] Featured Learning 特征学习[ ] Feedforward Neural Networks/FNN 前馈神经网络[ ] Fine-tuning 微调[ ] Flipping output 翻转法[ ] Fluctuation 震荡[ ] Forward stagewise algorithm 前向分步算法[ ] Frequentist 频率主义学派[ ] Full-rank matrix 满秩矩阵[400 ] Functional neuron 功能神经元Letter G[ ] Gain ratio 增益率[ ] Game theory 博弈论[ ] Gaussian kernel function 高斯核函数[ ] Gaussian Mixture Model 高斯混合模型[ ] General Problem Solving 通用问题求解[ ] Generalization 泛化[ ] Generalization error 泛化误差[ ] Generalization error bound 泛化误差上界[ ] Generalized Lagrange function 广义拉格朗日函数[ ] Generalized linear model 广义线性模型[ ] Generalized Rayleigh quotient 广义瑞利商[ ] Generative Adversarial Networks/GAN 生成对抗网络[ ] Generative Model 生成模型[ ] Generator 生成器[ ] Genetic Algorithm/GA 遗传算法[ ] Gibbs sampling 吉布斯采样[ ] Gini index 基尼指数[ ] Global minimum 全局最小[ ] Global Optimization 全局优化[ ] Gradient boosting 梯度提升[ ] Gradient Descent 梯度下降[ ] Graph theory 图论[ ] Ground-truth 真相／真实Letter H[ ] Hard margin 硬间隔[ ] Hard voting 硬投票[ ] Harmonic mean 调和平均[ ] Hesse matrix 海塞矩阵[ ] Hidden dynamic model 隐动态模型[ ] Hidden layer 隐藏层[ ] Hidden Markov Model/HMM 隐马尔可夫模型[ ] Hierarchical clustering 层次聚类[ ] Hilbert space 希尔伯特空间[ ] Hinge loss function 合页损失函数[ ] Hold-out 留出法[ ] Homogeneous 同质[ ] Hybrid computing 混合计算[ ] Hyperparameter 超参数[ ] Hypothesis 假设[ ] Hypothesis test 假设验证Letter I[ ] ICML 国际机器学习会议[450 ] Improved iterative scaling/IIS 改进的迭代尺度法[ ] Incremental learning 增量学习[ ] Independent and identically distributed/i.i.d. 独立同分布[ ] Independent Component Analysis/ICA 独立成分分析[ ] Indicator function 指示函数[ ] Individual learner 个体学习器[ ] Induction 归纳[ ] Inductive bias 归纳偏好[ ] Inductive learning 归纳学习[ ] Inductive Logic Programming／ILP 归纳逻辑程序设计[ ] Information entropy 信息熵[ ] Information gain 信息增益[ ] Input layer 输入层[ ] Insensitive loss 不敏感损失[ ] Inter-cluster similarity 簇间相似度[ ] International Conference for Machine Learning/ICML 国际机器学习大会[ ] Intra-cluster similarity 簇内相似度[ ] Intrinsic value 固有值[ ] Isometric Mapping/Isomap 等度量映射[ ] Isotonic regression 等分回归[ ] Iterative Dichotomiser 迭代二分器Letter K[ ] Kernel method 核方法[ ] Kernel trick 核技巧[ ] Kernelized Linear Discriminant Analysis／KLDA 核线性判别分析[ ] K-fold cross validation k 折交叉验证／k 倍交叉验证[ ] K-Means Clustering K –均值聚类[ ] K-Nearest Neighbours Algorithm/KNN K近邻算法[ ] Knowledge base 知识库[ ] Knowledge Representation 知识表征Letter L[ ] Label space 标记空间[ ] Lagrange duality 拉格朗日对偶性[ ] Lagrange multiplier 拉格朗日乘子[ ] Laplace smoothing 拉普拉斯平滑[ ] Laplacian correction 拉普拉斯修正[ ] Latent Dirichlet Allocation 隐狄利克雷分布[ ] Latent semantic analysis 潜在语义分析[ ] Latent variable 隐变量[ ] Lazy learning 懒惰学习[ ] Learner 学习器[ ] Learning by analogy 类比学习[ ] Learning rate 学习率[ ] Learning Vector Quantization/LVQ 学习向量量化[ ] Least squares regression tree 最小二乘回归树[ ] Leave-One-Out/LOO 留一法[500 ] linear chain conditional random field 线性链条件随机场[ ] Linear Discriminant Analysis／LDA 线性判别分析[ ] Linear model 线性模型[ ] Linear Regression 线性回归[ ] Link function 联系函数[ ] Local Markov property 局部马尔可夫性[ ] Local minimum 局部最小[ ] Log likelihood 对数似然[ ] Log odds／logit 对数几率[ ] Logistic Regression Logistic 回归[ ] Log-likelihood 对数似然[ ] Log-linear regression 对数线性回归[ ] Long-Short Term Memory/LSTM 长短期记忆[ ] Loss function 损失函数Letter M[ ] Machine translation/MT 机器翻译[ ] Macron-P 宏查准率[ ] Macron-R 宏查全率[ ] Majority voting 绝对多数投票法[ ] Manifold assumption 流形假设[ ] Manifold learning 流形学习[ ] Margin theory 间隔理论[ ] Marginal distribution 边际分布[ ] Marginal independence 边际独立性[ ] Marginalization 边际化[ ] Markov Chain Monte Carlo/MCMC 马尔可夫链蒙特卡罗方法[ ] Markov Random Field 马尔可夫随机场[ ] Maximal clique 最大团[ ] Maximum Likelihood Estimation/MLE 极大似然估计／极大似然法[ ] Maximum margin 最大间隔[ ] Maximum weighted spanning tree 最大带权生成树[ ] Max-Pooling 最大池化[ ] Mean squared error 均方误差[ ] Meta-learner 元学习器[ ] Metric learning 度量学习[ ] Micro-P 微查准率[ ] Micro-R 微查全率[ ] Minimal Description Length/MDL 最小描述长度[ ] Minimax game 极小极大博弈[ ] Misclassification cost 误分类成本[ ] Mixture of experts 混合专家[ ] Momentum 动量[ ] Moral graph 道德图／端正图[ ] Multi-class classification 多分类[ ] Multi-document summarization 多文档摘要[ ] Multi-layer feedforward neural networks 多层前馈神经网络[ ] Multilayer Perceptron/MLP 多层感知器[ ] Multimodal learning 多模态学习[550 ] Multiple Dimensional Scaling 多维缩放[ ] Multiple linear regression 多元线性回归[ ] Multi-response Linear Regression ／MLR 多响应线性回归[ ] Mutual information 互信息Letter N[ ] Naive bayes 朴素贝叶斯[ ] Naive Bayes Classifier 朴素贝叶斯分类器[ ] Named entity recognition 命名实体识别[ ] Nash equilibrium 纳什均衡[ ] Natural language generation/NLG 自然语言生成[ ] Natural language processing 自然语言处理[ ] Negative class 负类[ ] Negative correlation 负相关法[ ] Negative Log Likelihood 负对数似然[ ] Neighbourhood Component Analysis/NCA 近邻成分分析[ ] Neural Machine Translation 神经机器翻译[ ] Neural Turing Machine 神经图灵机[ ] Newton method 牛顿法[ ] NIPS 国际神经信息处理系统会议[ ] No Free Lunch Theorem／NFL 没有免费的午餐定理[ ] Noise-contrastive estimation 噪音对比估计[ ] Nominal attribute 列名属性[ ] Non-convex optimization 非凸优化[ ] Nonlinear model 非线性模型[ ] Non-metric distance 非度量距离[ ] Non-negative matrix factorization 非负矩阵分解[ ] Non-ordinal attribute 无序属性[ ] Non-Saturating Game 非饱和博弈[ ] Norm 范数[ ] Normalization 归一化[ ] Nuclear norm 核范数[ ] Numerical attribute 数值属性Letter O[ ] Objective function 目标函数[ ] Oblique decision tree 斜决策树[ ] Occam’s razor 奥卡姆剃刀[ ] Odds 几率[ ] Off-Policy 离策略[ ] One shot learning 一次性学习[ ] One-Dependent Estimator／ODE 独依赖估计[ ] On-Policy 在策略[ ] Ordinal attribute 有序属性[ ] Out-of-bag estimate 包外估计[ ] Output layer 输出层[ ] Output smearing 输出调制法[ ] Overfitting 过拟合／过配[600 ] Oversampling 过采样Letter P[ ] Paired t-test 成对t 检验[ ] Pairwise 成对型[ ] Pairwise Markov property 成对马尔可夫性[ ] Parameter 参数[ ] Parameter estimation 参数估计[ ] Parameter tuning 调参[ ] Parse tree 解析树[ ] Particle Swarm Optimization/PSO 粒子群优化算法[ ] Part-of-speech tagging 词性标注[ ] Perceptron 感知机[ ] Performance measure 性能度量[ ] Plug and Play Generative Network 即插即用生成网络[ ] Plurality voting 相对多数投票法[ ] Polarity detection 极性检测[ ] Polynomial kernel function 多项式核函数[ ] Pooling 池化[ ] Positive class 正类[ ] Positive definite matrix 正定矩阵[ ] Post-hoc test 后续检验[ ] Post-pruning 后剪枝[ ] potential function 势函数[ ] Precision 查准率／准确率[ ] Prepruning 预剪枝[ ] Principal component analysis/PCA 主成分分析[ ] Principle of multiple explanations 多释原则[ ] Prior 先验[ ] Probability Graphical Model 概率图模型[ ] Proximal Gradient Descent/PGD 近端梯度下降[ ] Pruning 剪枝[ ] Pseudo-label 伪标记[ ] Letter Q[ ] Quantized Neural Network 量子化神经网络[ ] Quantum computer 量子计算机[ ] Quantum Computing 量子计算[ ] Quasi Newton method 拟牛顿法Letter R[ ] Radial Basis Function／RBF 径向基函数[ ] Random Forest Algorithm 随机森林算法[ ] Random walk 随机漫步[ ] Recall 查全率／召回率[ ] Receiver Operating Characteristic/ROC 受试者工作特征[ ] Rectified Linear Unit/ReLU 线性修正单元[650 ] Recurrent Neural Network 循环神经网络[ ] Recursive neural network 递归神经网络[ ] Reference model 参考模型[ ] Regression 回归[ ] Regularization 正则化[ ] Reinforcement learning/RL 强化学习[ ] Representation learning 表征学习[ ] Representer theorem 表示定理[ ] reproducing kernel Hilbert space/RKHS 再生核希尔伯特空间[ ] Re-sampling 重采样法[ ] Rescaling 再缩放[ ] Residual Mapping 残差映射[ ] Residual Network 残差网络[ ] Restricted Boltzmann Machine/RBM 受限玻尔兹曼机[ ] Restricted Isometry Property/RIP 限定等距性[ ] Re-weighting 重赋权法[ ] Robustness 稳健性/鲁棒性[ ] Root node 根结点[ ] Rule Engine 规则引擎[ ] Rule learning 规则学习Letter S[ ] Saddle point 鞍点[ ] Sample space 样本空间[ ] Sampling 采样[ ] Score function 评分函数[ ] Self-Driving 自动驾驶[ ] Self-Organizing Map／SOM 自组织映射[ ] Semi-naive Bayes classifiers 半朴素贝叶斯分类器[ ] Semi-Supervised Learning 半监督学习[ ] semi-Supervised Support Vector Machine 半监督支持向量机[ ] Sentiment analysis 情感分析[ ] Separating hyperplane 分离超平面[ ] Sigmoid function Sigmoid 函数[ ] Similarity measure 相似度度量[ ] Simulated annealing 模拟退火[ ] Simultaneous localization and mapping 同步定位与地图构建[ ] Singular Value Decomposition 奇异值分解[ ] Slack variables 松弛变量[ ] Smoothing 平滑[ ] Soft margin 软间隔[ ] Soft margin maximization 软间隔最大化[ ] Soft voting 软投票[ ] Sparse representation 稀疏表征[ ] Sparsity 稀疏性[ ] Specialization 特化[ ] Spectral Clustering 谱聚类[ ] Speech Recognition 语音识别[ ] Splitting variable 切分变量[700 ] Squashing function 挤压函数[ ] Stability-plasticity dilemma 可塑性-稳定性困境[ ] Statistical learning 统计学习[ ] Status feature function 状态特征函[ ] Stochastic gradient descent 随机梯度下降[ ] Stratified sampling 分层采样[ ] Structural risk 结构风险[ ] Structural risk minimization/SRM 结构风险最小化[ ] Subspace 子空间[ ] Supervised learning 监督学习／有导师学习[ ] support vector expansion 支持向量展式[ ] Support Vector Machine/SVM 支持向量机[ ] Surrogat loss 替代损失[ ] Surrogate function 替代函数[ ] Symbolic learning 符号学习[ ] Symbolism 符号主义[ ] Synset 同义词集Letter T[ ] T-Distribution Stochastic Neighbour Embedding/t-SNE T –分布随机近邻嵌入[ ] Tensor 张量[ ] Tensor Processing Units/TPU 张量处理单元[ ] The least square method 最小二乘法[ ] Threshold 阈值[ ] Threshold logic unit 阈值逻辑单元[ ] Threshold-moving 阈值移动[ ] Time Step 时间步骤[ ] Tokenization 标记化[ ] Training error 训练误差[ ] Training instance 训练示例／训练例[ ] Transductive learning 直推学习[ ] Transfer learning 迁移学习[ ] Treebank 树库[ ] Tria-by-error 试错法[ ] True negative 真负类[ ] True positive 真正类[ ] True Positive Rate/TPR 真正例率[ ] Turing Machine 图灵机[ ] Twice-learning 二次学习Letter U[ ] Underfitting 欠拟合／欠配[ ] Undersampling 欠采样[ ] Understandability 可理解性[ ] Unequal cost 非均等代价[ ] Unit-step function 单位阶跃函数[ ] Univariate decision tree 单变量决策树[ ] Unsupervised learning 无监督学习／无导师学习[ ] Unsupervised layer-wise training 无监督逐层训练[ ] Upsampling 上采样Letter V[ ] Vanishing Gradient Problem 梯度消失问题[ ] Variational inference 变分推断[ ] VC Theory VC维理论[ ] Version space 版本空间[ ] Viterbi algorithm 维特比算法[760 ] Von Neumann architecture 冯· 诺伊曼架构Letter W[ ] Wasserstein GAN/WGAN Wasserstein生成对抗网络[ ] Weak learner 弱学习器[ ] Weight 权重[ ] Weight sharing 权共享[ ] Weighted voting 加权投票法[ ] Within-class scatter matrix 类内散度矩阵[ ] Word embedding 词嵌入[ ] Word sense disambiguation 词义消歧Letter Z[ ] Zero-data learning 零数据学习[ ] Zero-shot learning 零次学习第三部分A[ ] approximations近似值[ ] arbitrary随意的[ ] affine仿射的[ ] arbitrary任意的[ ] amino acid氨基酸[ ] amenable经得起检验的[ ] axiom公理，原则[ ] abstract提取[ ] architecture架构，体系结构；建造业[ ] absolute绝对的[ ] arsenal军火库[ ] assignment分配[ ] algebra线性代数[ ] asymptotically无症状的[ ] appropriate恰当的B[ ] bias偏差[ ] brevity简短，简洁；短暂[800 ] broader广泛[ ] briefly简短的[ ] batch批量C[ ] convergence 收敛，集中到一点[ ] convex凸的[ ] contours轮廓[ ] constraint约束[ ] constant常理[ ] commercial商务的[ ] complementarity补充[ ] coordinate ascent同等级上升[ ] clipping剪下物；剪报；修剪[ ] component分量；部件[ ] continuous连续的[ ] covariance协方差[ ] canonical正规的，正则的[ ] concave非凸的[ ] corresponds相符合；相当；通信[ ] corollary推论[ ] concrete具体的事物，实在的东西[ ] cross validation交叉验证[ ] correlation相互关系[ ] convention约定[ ] cluster一簇[ ] centroids 质心，形心[ ] converge收敛[ ] computationally计算(机)的[ ] calculus计算D[ ] derive获得，取得[ ] dual二元的[ ] duality二元性；二象性；对偶性[ ] derivation求导；得到；起源[ ] denote预示，表示，是…的标志；意味着，[逻]指称[ ] divergence 散度；发散性[ ] dimension尺度，规格；维数[ ] dot小圆点[ ] distortion变形[ ] density概率密度函数[ ] discrete离散的[ ] discriminative有识别能力的[ ] diagonal对角[ ] dispersion分散，散开[ ] determinant决定因素[849 ] disjoint不相交的E[ ] encounter遇到[ ] ellipses椭圆[ ] equality等式[ ] extra额外的[ ] empirical经验；观察[ ] ennmerate例举，计数[ ] exceed超过，越出[ ] expectation期望[ ] efficient生效的[ ] endow赋予[ ] explicitly清楚的[ ] exponential family指数家族[ ] equivalently等价的F[ ] feasible可行的[ ] forary初次尝试[ ] finite有限的，限定的[ ] forgo摒弃，放弃[ ] fliter过滤[ ] frequentist最常发生的[ ] forward search前向式搜索[ ] formalize使定形G[ ] generalized归纳的[ ] generalization概括，归纳；普遍化；判断（根据不足）[ ] guarantee保证；抵押品[ ] generate形成，产生[ ] geometric margins几何边界[ ] gap裂口[ ] generative生产的；有生产力的H[ ] heuristic启发式的；启发法；启发程序[ ] hone怀恋；磨[ ] hyperplane超平面L[ ] initial最初的[ ] implement执行[ ] intuitive凭直觉获知的[ ] incremental增加的[900 ] intercept截距[ ] intuitious直觉[ ] instantiation例子[ ] indicator指示物，指示器[ ] interative重复的，迭代的[ ] integral积分[ ] identical相等的；完全相同的[ ] indicate表示，指出[ ] invariance不变性，恒定性[ ] impose把…强加于[ ] intermediate中间的[ ] interpretation解释，翻译J[ ] joint distribution联合概率L[ ] lieu替代[ ] logarithmic对数的，用对数表示的[ ] latent潜在的[ ] Leave-one-out cross validation留一法交叉验证M[ ] magnitude巨大[ ] mapping绘图，制图；映射[ ] matrix矩阵[ ] mutual相互的，共同的[ ] monotonically单调的[ ] minor较小的，次要的[ ] multinomial多项的[ ] multi-class classification二分类问题N[ ] nasty讨厌的[ ] notation标志，注释[ ] naïve朴素的O[ ] obtain得到[ ] oscillate摆动[ ] optimization problem最优化问题[ ] objective function目标函数[ ] optimal最理想的[ ] orthogonal(矢量，矩阵等)正交的[ ] orientation方向[ ] ordinary普通的[ ] occasionally偶然的P[ ] partial derivative偏导数[ ] property性质[ ] proportional成比例的[ ] primal原始的，最初的[ ] permit允许[ ] pseudocode伪代码[ ] permissible可允许的[ ] polynomial多项式[ ] preliminary预备[ ] precision精度[ ] perturbation 不安，扰乱[ ] poist假定，设想[ ] positive semi-definite半正定的[ ] parentheses圆括号[ ] posterior probability后验概率[ ] plementarity补充[ ] pictorially图像的[ ] parameterize确定…的参数[ ] poisson distribution柏松分布[ ] pertinent相关的Q[ ] quadratic二次的[ ] quantity量，数量；分量[ ] query疑问的R[ ] regularization使系统化；调整[ ] reoptimize重新优化[ ] restrict限制；限定；约束[ ] reminiscent回忆往事的；提醒的；使人联想…的（of）[ ] remark注意[ ] random variable随机变量[ ] respect考虑[ ] respectively各自的；分别的[ ] redundant过多的；冗余的S[ ] susceptible敏感的[ ] stochastic可能的；随机的[ ] symmetric对称的[ ] sophisticated复杂的[ ] spurious假的；伪造的[ ] subtract减去；减法器[ ] simultaneously同时发生地；同步地[ ] suffice满足[ ] scarce稀有的，难得的[ ] split分解，分离[ ] subset子集[ ] statistic统计量[ ] successive iteratious连续的迭代[ ] scale标度[ ] sort of有几分的[ ] squares平方T[ ] trajectory轨迹[ ] temporarily暂时的[ ] terminology专用名词[ ] tolerance容忍；公差[ ] thumb翻阅[ ] threshold阈，临界[ ] theorem定理[ ] tangent正弦U[ ] unit-length vector单位向量V[ ] valid有效的，正确的[ ] variance方差[ ] variable变量；变元[ ] vocabulary词汇[ ] valued经估价的；宝贵的[ ] W [1038 ] wrapper包装。
Wavelet计算信号处理说明书
A. Aldroubi, Oblique and hierarchical multiwavelet bases, To appear in Applied and Camp. Harmonic Analysis, 1997.
J. Allen, Cochlear modeling, IEEE ASSP Magazine, 2:3-29, 1985.
[BMG92] A. Baskurt, I. E. Magnin, and R. Goutte, Adaptive discrete cosine transform coding algorithm for digital mammography, Optical Engineering, 31:1922-1928, Sept. 1992.
A. Aldroubi and M. Unser, Sampling procedures in function spaces and asymptotic equivalence with Shannon's sampling theory, Numer. Funct. Anal. Optimiz., 15:1-21, 1994.
[BT92]
J. J. Benedetto and A. Teolis, An auditory motivated time scale signal representation, IEEE-SP International Symposium on Time-Frequency and Time-Scale analysis, Oct. 1992.
基于高斯过程建模的物联网数据不确定性度量与预测
服从零 状态 x i 的预测响应。 ε 为加性白噪声误差， 2 2 均值方差为 σ n 的高斯分布， 即 p （ ε） ～ N （ 0， σn ） 。 f（ x） 为反映农业对象的动态时间序列特性的隐函 数， ε 与 f（ x） 相互独立。 本文使用高斯过程用于动 态系统的建模， 以确定一个目标函数 f （ x ） 用于逼 近函数 f（ x） ， 使其能最好地表示输入和输出间产周期长、 影响因子复杂， ［1 ］ 关系十分困难， 通过大数据技术 促进农业生产与 发展 的 潜 力 已 经 初 现。 随 着 精 准 农 业、 智慧农 ［2 ］ ， 、 业 物联网和云计算的快速发展 农业数据也呈 现出爆炸式的增加。 物联网 已经成为农业大数 据最重要的数据采集工具之一。
［4 － 5 ］ ［3 ］
1
时间序列数据的动态建模
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现代农业中大量运用各类传感器实现大田种 ［6 － 7 ］ ［8 － 9 ］ 、 和水产养殖 等农业信息 设施园艺
针对时间序列建模与预测通常有 2 种方法： 一 种是将时间序列 t 作为输入变量， 建立静态回归模 型， 用于时间序列的预测； 另一种是建立时间序列动 态模型， 即建立由历史数据对当前数据的多步预测 模型。本文只考虑后一种模型的基于高斯过程的非 线性动态建模方法。
Uncertainty Measurement and Prediction of IOT Data Based on Gaussian Process Modeling
2 Yuan Jin1，
Hu Min1
Kesheng Wang3
2 Liu Xuemei1，
2 Hou Jialin1，
Mi Qinghua4
t y t2 ， …， y t L 和自回归 考虑一个时间序列数据 y 1 ，
接触问题的罚函数法
BULETINUL INSTITUTULUI POLITEHNIC DIN IAŞIPublicat deUniversitatea Tehnică …Gheorghe Asachi” din IaşiTomul LIV (LVIII), Fasc. 3, 2011SecţiaCONSTRUCŢII. ARHITECTURĂPENALTY BASED ALGORITHMS FOR FRICTIONALCONTACT PROBLEMSBYANDREI-IONUŢŞTEFANCU*, SILVIU-CRISTIAN MELENCIUCand MIHAI BUDESCU“Gheorghe Asachi” Technical University of Iaşi,Faculty of Civil Engineering and Building ServicesReceived: June 21, 2011Accepted for publication: August 29, 2011Abstract. The finite element method is a numerical method that can be successfully used to generate solutions for problems belonging to a vast array of engineering fields: stationary, transitory, linear or nonlinear problems. For the linear case, computing the solution to the given problem is a straightforward process, the displacements are obtained in a single step and all the other quantities are evaluated afterwards. When faced with a nonlinear problems, in this case with a contact nonlinearity, one needs to account for the fact that the stiffness matrix of the systems varies with the loading, the force vs. stiffness relation being unknown prior to the beginning of the analysis. Modern software using the finite element method to solve contact problems usually approaches such problems via two basic theories that, although different in their approaches, lead to the desired solutions. One of the theories is known as the penalty function method, and the other as the Lagrange multipliers method. The hereby paper briefly presents the two methods emphasizing the penalty based ones. The paper also underscores the influence of input parameters for the case of the two methods on the results when using the software ANSYS 12.Key words: finite element method; pure penalty methods; Lagrange multipliers method; H-adaptive meshing.*Corresponding author: e-mail: stefancu@ce.tuiasi.ro120Andrei-IonuţŞtefancu, Silviu-Cristian Melenciuc and Mihai Budescu1. IntroductionThe finite element method (FEM) is a numerical method that can be applied to obtain solutions to problems belonging to a variety of engineering disciplines: stationary problems, transitory problems, linear or nonlinear stress/strain problems. Heat transfer, fluid flow, or electromagnetism problems can also be solved using FEM.The first ones to publish articles in this field are Alexander Hrennikoff (1941) and Richard Courant (1943). Although their approaches are different, they have a common feature – discretization of continua into a series of discrete sub-domains called elements. Olgierd Zienkiewicz summarizes the work of his predecessors in what was to be known as FEM (1947). In 1960 Ray Clough is the first one to use the term finite element. Zienkiewicz and Cheung published in 1967 the first book entirely devoted to the finite element method.In this context, in 1971, the first version of the ANSYS software is released. Currently, the ANSYS software package is able to solve static, dynamic, heat transfer, fluid flow, electromagnetics problems, etc. ANSYS is a market leader for more than 20 years (Moavenim, 1999). In the present study version 12.0.1 of the software was used (ANSYS Workbench 2.0, 2009).2. Finite Element Formulations of Contact ProblemsThere are two basic theories that, although different in their approaches, offer the desired solutions to body contact problems: the penalty function method and the Lagrange multipliers method.The main difference between them is the way they include in their formulation the potential energy of contacting surfaces.The penalty function method, due to its economy, has received a wider acceptance. The method is very useful when solving frictional contact problems, while the Lagrange method, based on multipliers, is known for its accuracy.The main drawback of the Lagrange method is that it may lead to ill-converging solutions while the penalty formulation may lead to inaccurate ones.In the following the pure penalty, the augmented Lagrange methods will be presented.2.1. The Penalty MethodThe penalty method involves adding a penalty term to enhance the solving process. In contact problems the penalty term includes the stiffness matrix of the contact surface. The matrix results from the concept that one body imaginary penetrates the another (Wriggers et al., 1990).Bul. Inst. Polit. Ia şi, t. LVII (LXI), f. 3, 2011 121The stiffness matrix of the contact surface is added to the stiffness matrix of the contacting body, so that the incremental equation of the Finite Element becomes[K b + K c ]u = F , (1)where: K b is the stiffness matrix of contacting bodies; K c – stiffness matrix of contact surface; u – displacement; F – force.The magnitude of the contact surface is unknown (Stein & Ramm, 2003), therefore its stiffness matrix, K c , is a nonlinear term. The total load and displacement values are∑Δ=F F tot , (2)∑Δ=u u tot, (3)where: F tot is the force vector; u tot – displacement vector.To derive the stiffness matrix, the contact zone (encompassing the contact surface) is divided into a series of contact elements. The element represents the interaction between the surface node of one body with the respective element face of the other body. Fig. 1 shows a contact element in a two dimensional application. It is composed of a slave node (point S ) and a master line, connecting nodes 1 and 2. S 0 marks the slave node before the application of the load increment, and S marks the node after loading.Fig. 1 – Contact element – penalty method formulation.Given the nature of the numerical simulations presented afterwards only the sliding mode of friction will be presented. In this case, the tangential force acting at the contact surface equals the magnitude of the friction force, hence the first variation of the potential energy of a contact element ist n n d t t n n t t n n c g g k g g g k g f g f δμδδδδ)sgn( +=+=Π, (4)122 Andrei-Ionu ţ Ştefancu, Silviu-Cristian Melenciuc and Mihai Budescuwhere: k n represents penalty terms used to express the relationship between the contact force and the penetrations along the normal direction; k t – penalty terms used to express the relationship between the contact force and the penetrations along the tangential direction; g n – penetration along the normal direction; g t – penetration along the tangential direction;n n n f k g =, (5))()sgn(n n d t t g k g f μ−=. (6)2.2. The Augmented Lagrange Multiplier MethodIn the case of classical Lagrange Multiplier Method the contact forces are expressed by Lagrange multipliers. The augmented Lagrange method involves the regularization of classical Lagrange method by adding a penalty function from the penalty method (Simo & Laursen, 1992). This method, unlike the classical one, can be applied to sticking friction, sliding friction, and to africtionless contactFig. 2 – Contact element – Lagrangian methods.The contact problem involves the minimization of potential П by equating to zero the following expression:kg g g u u T T b 21)(),(+Λ+Π=ΛΠ, (7) where ⎥⎥⎦⎤⎢⎢⎣⎡⎭⎬⎫⎩⎨⎧⎭⎬⎫⎩⎨⎧⎭⎬⎫⎩⎨⎧=Λk t k n t n t n Tλλλλλλ,...,,2211, (8) with:n λ – Lagrange multiplier for the normal direction;t λ– Lagrange multiplier for the tangential direction;Bul. Inst. Polit. Ia şi, t. LVII (LXI), f. 3, 2011 12312k n n n 12k t t t g g g g ,,...,g g g ⎡⎤⎧⎫⎧⎫⎧⎫⎪⎪⎪⎪⎪⎪=⎢⎥⎨⎬⎨⎬⎨⎬⎢⎥⎪⎪⎪⎪⎪⎪⎩⎭⎩⎭⎩⎭⎣⎦. (9)3. Parametric Analysis of Frictional ContactIn order to illustrate the way that the contact algorithms may influence the results a parametric analysis is performed. The purpose of this analysis is to exemplify how various input parameters can alter the results.3.1. Finite Elements Formulations of Contact Problems in ANSYSThe Finite Elements (FE) software ANSYS, for the penalty method, assumes that contact force along the normal direction is written as follows:cont.penetr.cont.F x K Δ=Δ, (10)where: K cont. is the contact stiffness, defined by real constant FKN for the 17x contact elements (in the current analysis the 174 contact element is used); x penetr. – distance between two existing nodes on separate contact bodies; F cont. – contact force. ANSYS automatically chooses the real constant FKN as a scale factor of the stiffness of the underlying elements. This value can be modified by the user (via FKN – a scale factor).Given the fact that the augmented Lagrange method is actually a penalty method with penetration control, the contact force is computed according to eq. (10), the only difference being the contact stiffness formulationpenetr.cont.1x K i i +=+λλ, (11)where λi is a Lagrange multiplier.Although the Lagrange multipliers are condensed out at the element level, one can think regarding this method as the same as a regular penalty one except that the contact stiffness is “updated” per contact element (Imaoka, 2001).Similar to the normal direction, a real constant – FKT models the tangential stiffness of the contact.3.2. Adaptive Solutions in ANSYSIn order to overcome the influence of the meshing upon the final results of the analysis and to improve the accuracy of the solution an adaptive solution will be used.124 Andrei-Ionu ţ Ştefancu, Silviu-Cristian Melenciuc and Mihai BudescuIn ANSYS the desired accuracy of a solution can be achieved by means of adaptive and iterative analysis, whereby h -adaptive methodology is employed.The h -adaptive method begins with an initial FE model that is refined over various iterations by replacing coarse elements with finer ones in selected regions of the model. This is effectively a selective remeshing procedure.The criterion for which elements are selected for adaptive refinement depends on geometry and, for the current analysis, on a 10% allowable difference between the maximum values of the frictional (obtained in two consecutive runs with different meshes).The user-specified accuracy is achieved when convergence is satisfied as follows:)in ...,3,2,1( ,1001R n i E i i i −=<⎟⎠⎞⎜⎝⎛−+φφφ, (11) where:φis the result quantity; E – expected accuracy (10% for this case); R – the region on the geometry that is being subjected to adaptive analysis (entire geometry in this case); i – the iteration number.The results are compared from iteration i to iteration i + 1. Iteration in this context includes a full analysis in which h -adaptive meshing and solving are performed.For this case of adaptive procedures, the ANSYS product identifies the largest elements, which are deleted and replaced with a finer FE representation (ANSYS, 2009).The overall results show a good behavior of the model. Only two iteration are performed in order to satisfy reach the expected accuracy of the solution.Table 1 h-Adaptive Methodology Convergence HistoryIteration Frictional Stress, [MPa]Change, [%] Nodes Elements 1 0.130042,518 352 2 0.12739–2.0571 14,158 8,2343.3. The Model Used in the Parametric AnalysisThe model used, represented in Fig. 3, comprises two solids made up of nonlinear structural steel materials. The larger solid has its lower surface fixed while at the upper end interacts with the smaller solid via a frictional contact (coefficient of friction 0.2). A normal pressure of 0.5 MPa is applied on top of the smaller solid, and displacement is applied on the left hand side face.Bul. Inst. Polit. Iaşi, t. LVII (LXI), f. 3, 2011 125Fig. 3 – The model used in the parametric analysis. A – frictionalcontact; B – applied pressure; C – fixed support; D – applieddisplacement.Input parameters:a) FE formulation (P1); this parameter can take two values: 0 for augmented Lagrange method and 1 for pure penalty method;b) the normal contact stiffness factor FKN (P2) that varies between 0.01 and 1;c) the tangent contact stiffness factor FKT (P3) that varies between 0.01 and 1.Output parameters: a number of output parameters have been monitored, such as: maximum (P5) and minimum (P8) normal elastic strain, maximum (P9) and minimum (P10) shear elastic strain, maximum (P12) and minimum (P13) normal stress, maximum (P14) and minimum (P15) shear stress, maximum (P11) frictional stress, maximum (P6) penetration, analysis run time (P7), maximum stiffness energy (P16).3.4. Results of the Parametric AnalysisThe parametric analysis provides a wide range of information regarding the dependence of the output parameters on the input ones. Based on the relevance of the results only a limited amount of them will be presented.The local sensitivity chart allows one to appreciate the impact of the input parameters on the output ones. This means that the output is computed based on the change of each input independently of the current value of each input parameter. The larger the change of the output, the more significant is the input parameter that was varied (ANSYS, 2009). Since the local sensibilities can only be computed for continuous parameters (P1 is a discrete one) the sensibility chart will be presented for the pure penalty and augmented Lagrange method individually.126Andrei-IonuţŞtefancu, Silviu-Cristian Melenciuc and Mihai BudescuFig. 4 – Local sensibility chart – Lagrangian method.Fig. 5 – Local sensibility chart – pure penalty method.Bul. Inst. Polit. Iaşi, t. LVII (LXI), f. 3, 2011 127It can be seen, from Fig. 5, that the pure penalty method is less sensitiveto contact normal and tangent stiffness than the augmented Lagrange method.The only output parameter influenced by the contact stiffness is the analysisrun-time.In Figs. 4 and 5 only sensitivities of the three parameters (P5, P6, P7)have been presented because the sensitivities of the other are zero or almostzero. Based on this the variation of P5, P6 and P7, with P2 and P3, arepresented in what follows.a bFig. 6 – Variation of P5 with P2 and P3: a – augmented Lagrangemethod; b – pure penalty method.a bFig. 7 – Variation of P6 with P2 and P3: a – augmented Lagrangemethod; b – pure penalty method.As it can be seen from Figs. 6 and 7 there isn’t an exact pattern of thevariation of the maximum normal elastic strain (P5), maximum penetration128Andrei-IonuţŞtefancu, Silviu-Cristian Melenciuc and Mihai Budescu(P6) or analysis run time (P7) with the normal (P2) and tangent (P3) contactstiffness factor.Give the kinematic nature of the problem, and the contact type(frictional) it can be observed from Fig. 8 that the analysis run time (the timeneeded to compute a solution for the given problem) is tangent stiffnessdependent.a bFig. 8 – Variation of P7 with P2 and P3: a – augmented Lagrangemethod; b – pure penalty method.4. ConclusionsGiven the high nonlinear characteristic of the frictional contacts an extraattention is necessary to be paid to contact algorithms and their inputparameters. In such case an h-adaptive solution is recommended to be usedbecause such approach can “fade out” the influence of contact parameters onmost of the output parameters.If working circumstances require fulfilling certain limitations, accuracyconditions may be enforced, thus improving the confidence level of the finalsolution. One must keep in mind though, that an increased number of accuracyconvergence conditions leads to prohibitive analysis run time.REFERENCESClough R.W., The Finite Element Method in Plane Stress Analysis. Proc. 2nd ASCEConf. on Electron. Comp., Pittsburg,Pennsylvania, 1960.Courant R., Variational Methods for the Solution of Problems in Equilibrum and Vibra-tions. Bul. of the Amer. Mathem. Soc., 49,1-23 (1943).Hrennikoff A., Solution of Problems of Elasticity by the Frame-Work Method. ASME,J. of Appl. Mech., 8, 169–175 1(941).Bul. Inst. Polit. Ia şi, t. LVII (LXI), f. 3, 2011 129 Imaoka S., Sheldon’s ANSYS Tips and Tricks: Understanding Lagrange Multipliers .available on-line at /ansys/tips_sheldon/STI07_Lagrange_Mul-tipliers.pdf , 2001.Moavenim A., Finite Element Analysis – Theory and Applicaion with ANSYS . PrenticeHall, Upper Saddle River, New Jersey, 1999.Simo J.C., Laursen T.A., An Augmented Lagrangian Treatment of Contact ProblemsInvolving Friction . Comp. a. Struct., 42, 1, 97-116 (1992).Stein E., Ramm E., Error-Controlled Adaptive Finite Elements in Solid Mechanics . J.Wiley a. Sons, NY, 2003.Wriggers P., Vu Van T., Stein E., Finite Element Formulation of Large DeformationImpact-Contact Problems with Friction . Comp. a. Struct., 37, 3, 319-331 (1990).Zienkiewicz O.C., Cheung Y.K., The Finite Element Method in Continuum andStructural Mechanics . McGraw Hill, NY, 1967.Zienkiewicz O.C., The Stress Distribution in Gravity Dams . J. Inst. Civ. Engng., 27,244-271 (1947). * * * ANSYS Workbench 2.0 Framework Version: 12.0.1. ANSYS Inc., 2009. * * * Design Exploration . ANSYS Inc., 2009. * * * Theory Reference, Analysis Tools, ANSYS Workbench Product Adaptive Solutions . ANSYS Inc., 2009.ALGORITMI BAZA ŢI PE METODA COREC ŢIILOR UTILIZA ŢI ÎNREZOLVAREA PROBLEMELOR DE CONTACT CU FRECARE(Rezumat)Metoda elementului finit este o metod ă numeric ă ce poate fi aplicat ă cu succes pentru a ob ţine solu ţiile problemelor dintr-o multitudine de discipline inginere şti: probleme sta ţionare, probleme tranzitorii, liniare sau neliniare. În cazul liniar g ăsirea solu ţiei unei probleme date este un proces simplu. Deplas ările sunt ob ţinute într-un singur pas de analiz ă, tensiunile şi deforma ţiile fiind evaluate ulterior. În cazul problemelor neliniare – în acest caz neliniaritate de contact – trebuie s ă se ţin ă cont de faptul c ă matricea de rigiditate a sistemului variaz ă func ţie de înc ărcare, rela ţia for ţă vs . rigiditate nefiind cunoscut ă a priori . Programele moderne, ce folosesc metoda elementului finit pentru a rezolva probleme de contact, abordeaz ă de obicei astfel de probleme prin intermediul a dou ă teorii care, de şi diferite în abord ările lor, conduc la solu ţia dorit ă. Una dintre teorii este cunoscut ă sub numele de metoda corec ţiilor, iar cealalt ă ca metoda multiplicatorilor Lagrange. În lucrare se prezint ă pe scurt cele dou ă metode, accentul punându-se pe metodele bazate pe corec ţii. Lucrarea eviden ţiaz ă, de asemenea, influen ţa parametrilor de intrare caracteristici algoritmilor de rezolvare a problemelor de contact asupra rezultatelor atunci când se utilizeaz ă pachetul software ANSYS 12.。
Iterative methods for ill-conditioned Toeplitz matrices
P
2
2. Matrices related to Fast Transforms
To derive preconditioners for Toeplitz matrices we consider the following Transforms and related matrix classes: (I) The matrix 1 ijk n?1 Fn = pn exp(? 2 n ) j;k=0 ;
Iterative methods for ill-conditioned Toeplitz Matrices
Thomas Huckle Institut fur Informatik Technical University Munchen D-80333 Munchen, Germany
Toeplitz systems. We consider Toeplitz matrices with a real generating function that is nonnegative with only a small number of zeros. Then we can de ne a preconditioner of the form Sn Sn where Sn is the matrix describing the discrete Sine transform and is a diagonal matrix. If we have full knowledge about f then we can show that the preconditioned system is of bounded condition number independly of n. We can obtain the same result for the case that we know only the position and order of the zeros of f . If we only know the matrix and its coe cients tj , we present Sine transform preconditioners that show in many examples the same numerical behaviour. Key Words. Toeplitz matrix, Sine Transform, preconditioned conjugate gradient method
赋形波束天线设计讲稿
3.4 等距阵仅相位加权波束赋形 a) Stationary phase
Reference: 1) Chakraborty A, et al. Determination of phase functions for a desired one-dimensional pattern[J]. IEEE Transactions on Antennas and Propagation, 1981, 29(3): 502-506. 2) Chakraborty A, et al. Beam shaping using nonlinear phase distribution in a uniformly spaced array[J]. IEEE Transactions on Antennas and Propagation, 1982, 30(5): 1031-1034.
Reference: 1) Marcano D, Duran F. Synthesis of antenna arrays using genetic algorithms[J]. IEEE Antennas and Propagation Magazine, 2000, 42(3): 12-20. (复加权) 2) Boeringer D W, Werner D H. Adaptive mutation parameter toggling genetic algorithm for phase-only array synthesis[J]. Electronics Letters, 2002, 38(25): 1618-1619. 3) 徐慧, 李建新, 胡明春. 星载SAR波束展宽研究[A]. 2005年中国合成孔径雷达会议论文集[C]. 南京: 中国电子学会无线电定位技术分会, 2005. 27-31.
电子信息科学与技术专业外语单词汇总
[image understanding图像理解]
[interstate highway州际公路]
[in a nutshell简而言之]
[iterative method迭代法]
[information transfer信息传递]
[international standard国际标准]
[Parseval’s theorem巴塞瓦尔定理]
[quantum level 量化电平]
[RAM随机存取存储器,内存（random access memory)]
[round off舍入，用四舍五入化为整数]
[radio wave无线电波]
[radiating power发射功率]
[random pulses随机脉冲]
[ALU算术逻辑单元,运算器(arithmetic logic unit)]
[ASIC专用集成电路application specific integrated circuit]
[ADPCM自适应差分脉码调制(adaptive differential PCM)]
[ALU算术逻辑单元，运算器(arithmetic logic unit)]
[network operator网络运营商，网络操作员]
[nonlinear operation 非线性运算]
[optical receiver光接收器]
[open architecture开放型结构]
[OOP面向对象程序设计(object oriented programming)]
[log pulse PCM quantizer对数脉冲PCM量化器]
约翰克兰干气密封
John Crane – Test Failures
T O T AL % Fa ilu re s f ro m t h e T es t Ba y f or 20 0 4 /5/6
3 0 .0 2 5 .0
John Crane – CARs
CAR Issues raised per m onth for 2005/6
寿命
John Crane – QDC
John Crane – Vendor Rating
Averaged Vendor Rating & On-time Deliveries for Key Suppliers in 2006
Vendor Score % On-time
- - - - Vendor Rating Target Score
14 12 10 8 6 4
F
Qty
2 0
Ja n05 Fe b05 M ar -0 5 Ap r-0 5 M ay -0 5 Ju n05 Ju l-0 5 Au g05 Se p05 O ct -0 5 N ov -0 5 D ec -0 5 Ja n06 Fe b06 M ar -0 6 Ap r-0 6
静止密封头设计确定了密封的最大适应能力
Logarithmic spiral or bidirectional design. 对数螺旋槽或者 双向螺旋槽设计 Tungsten carbide or silicone carbide rotating seat 旋转环材料使用碳 化钨或者碳化硅
Tolerance ring to centralise seat on sleeve allowing thermal, pressure and centrifugal deflections. 在轴套上安装调心环，帮助静环中心定位。这种设计容 许由于热、压力或者离心力造成的偏差。
机械专业英语试题及答案
课程机械工程专业英语课程性质（□必修□限选□任选）考试方式（□闭卷□开卷）一、选择题（在每题四个备选答案中选出一个正确答案，本大题共15小题，每小题1分，总计15分）1. If all points in a linkage move in parallel planes the system undergoes planar motion and the linkage may be described as a .A. planar motionB. planar linkageC. jointsD. slide2. Though frame material and design should handle damping, are sometimes built into frame sections to handle specific problems.A .beams B. holes C. dampers D. screw3. The maximum allowable deflection of a shaft ______ determined by critical speed, gear, or bearing requirements.A. oftenB. mustC. wasD. is usually4. We will have to _____ to better and better solutions as we generate more information.A. repeat many timesB. iteratingC. iterateD. try ways5. If a product configuration is _______ specified and then examined to determine whether the performance requirements are met.A. finallyB. tentativelyC. temporaryD. have been6. Manufacturing can be defined as the _____ of raw materials into useful products through the use of the easiest and lest-expensive methods.A. transformationB. processingC. processD. transforming7. The planer and knee types of milling machine is _____ because of its flexibility.A. used most commonlyB. most popularC. the most commonly usedD. most powerful8. As a result, the system will vibrate at the frequency of the _____ force regardless of the initial conditions or natural frequency of the system.A. actionB. excitationC. outD. act9. Before two components are assembled together, the relationship between the dimensions of the mating surfaces_______.A. must be giving outB. should printed clearlyC. must be specifiedD. should be clearly noted10. The main practical advantage of lower pairs is their better ability to trap lubricant between their ______ surfaces.A. envelopingB. matingC. outerD. outside11. The word______ itself usually refers to the deterioration of metals and ceramics, while similar phenomena in plastics generally called ______.A. recrystallization…. corrosionB. recrystallization…degradationC. degradation…corrosionD. corrosion… degradation12. Most frames _______ cast iron, weld steel, composition, or concrete.A. are made fromB. are made up ofC. was produced byD. was consist of13. Rotating shafts particularly those that run at high speeds, must be designed to avoid operationat speeds.A . lowB .overload C. critical D. hollow14. Although cast iron is a fairly cheap material, each casting requires a .A. patternB. modelC. moldD. patent15. The term is used to describe joints with surface contact, as with a pin surrounded by a hole.A. high pairB. low pairC. surface pairD. rotary pair二、完型填空（在每个小题四个备选答案中选出一个正确答案，本大题共15小题，每小题1分，总计15分）The term shaft usually refers to a relatively long member of round cross section that rotates and transmits power. One or more members such as gears, sprockets, pulleys, and cams are usually (16) to the shaft by means of pins, keys, splines, snap rings, and other devices. These latter members are among the “associated parts” considered in this text, as are couplings and universal joint, which serve to (17) the shaft to the source of power or load.A shaft can have a noround cross section, ant it need not necessarily rotate. It can be stationary andserve to (18) a rotating member, such as the short shafts that support the nondriving wheels of an automobile. The shafts supporting(19) gears can be either rotating or stationary depending on (20) the gear is attached to the shaft or supported by it through bearings.It is apparent that shafts can be subjected to various combinations of axial, bending, and(21) loads, and that these loads may be static or fluctuating. Typically, a rotating shaft transmitting power is(22) to a constant torque (producing a mean torsional stress) together with a completely reversedbending load (producing an alternating bending stress).In addition to satisfying (23) requirements, shafts must be designed so that deflections are within acceptable limits. Excessive (24) shafts deflection can hamper gear performance and cause (25) noise. The associated angular deflection can be very destructive to no-self- aligning bearings(either plain or rolling). (26) deflection can affect the accuracy of a cam or gear driven mechanism.Furthermore, the greater the flexibility -either lateral or torsional-the lower the corresponding (27) speed.考生注意：考试时间120 分钟试卷总分100 分共 5 页第 1 页Sometimes members like gears and cams are made (28) with shaft, but moreoften such members (which also include pulleys, sprockets, etc)are madeseparately and then(29) shaft. The portion of the mounted member in contactwith the shaft is the hub. Attachment of the hub to the shaft is made in variety ofways. A gear can be gripped (30) between a shoulder on the shaft and a spacer,with torque being transmitted through a key. The grooves in the shaft and hub intowhich the key fits are called keyways.16、A) cement B) attached C) connected D) concrete17、A) endure B) transmit C) serve D) connect18、A) support B) meet C) satisfy D) strong19、A) aims B) idler C) terminal D) tomb20、A) due to B) weather C) whether D) for21、A) elasticity B) torsional C) inertia D) acceleration22、A) subjected B)…connect C) and D) From23、A) deeply B) strength C) clearly understand D) long time24、A) identify B) cross C) round D) lateral25、A) lucrative B) objectionable C) attractable D) terrible26、A) Moment B)Torment C) Torsional D) Movement27、A) high B) critical C) first D) important28、A) work B) idealization C) integral D) simplification29、A) mounted onto B) refers C) referring to D) indicates30、A) deformations B) bending limits C) laternal D) axially三、阅读理解（在每个小题四个备选答案中选出一个正确答案，填在题头的表中）（本大题共20小题，每小题2分，总计40分）Text 1Engineering design is a systematic process by which solution to the needs of humankind are obtained .The process is applied to problems(needs) of varying complexity .For example, mechanical engineers will use the design process to find an effective, efficient method to convert reciprocating motion to circular motion for the drive train in an intnternal combustion engine;electrical engineers will use the process to design electrical generating systems using falling water as the power source; and materials engineers use the process to design ablative materials which enable astronaut s to safely reenter the earth’s atmosphere.The vast majority of complex problems in today’s high technology society depend for solution not on a single engineering discipline, but on teams of engineers, scientists, environmentalists, economists, sociologists, and legal personnel. Solutions are not only dependent regulations and political influence. As engineers we are empowered with the technical expertise to develop new and improved products and systems, but at the same time we must be increasingly aware of the impact of our actions on society and the environment in general and work conscientiously toward the best solution in view of all relevant factors.Design is the culmination of engineering educational process; it is the salient feature that distinguishes engineering design is found in the curriculum guidelines of the Accreditation Board for Engineering and Technology(ABET).ABET accredits curricula in engineering schools and derives its membership from the various engineering professional societies. Each accredited curriculum has a well-defined design component which falls within the ABET statement on design 订线read as follows.Engineering design is the process of devising a system, component, or process to meet desired needs. It is decision making process(often iterative),in which the basic sciences, mathematics, and engineering sciences are applied to convert resources optimally to meet a sated objectives and criteria, synthesis, analysis, construction ,testing, and evaluation. The engineering design component of a curriculum must include most of the following features : development of student creativity, use of open-ended problem statement and use of modern design theory and methodology, formulation of design problem statement and specification, consideration of alternative solutions, feasibility considerations, production process, concurrent engineering design, and detailed system descriptions. Further, it is essential to include a variety of realistic constraints such as economic factors, safety, reliability, aesthetics, ethics, and social impact.31、what’s the main meaning of message (1)?A) Engineering design is a systematic process B) materials engineer’s main workC) mechanical engineer’s has good idea D) electrical engineer’ main duty32、In the author’s opinion, the design process is very complex and should taken into account many factors, from themessage who should NOT join into the design team _______.A) environmentalists,B) sociologistsC) engineersD) Government officials33、From the message, we can know the meaning of words “ABET”(at line third paragraph) is _____.A) a set of accredits standardB) a kind of design methodC) a department of US government which responsible for engineering designD) A set of law34、It can be concluded from the passage that in the exercise and training of student, which character of the following isNOT included in the curriculum ______.A) creativityB) new materialC) ethicsD) economic factors35、The title of the message is ______.A) the roles of engineers in manufacturingB) the importance of mechanical designC) engineering designD) The process of machine designTEXT 2Working drawings are the complete set of standardized drawings specifying the manufacture and assembly of a product based on its design. The complexity of the design determines the number and types of drawings. Working drawings may be on more than one sheet and may contain written instructions called specifications.Working drawings are blueprints used for manufacturing products. Therefore, the set of drawings must:(a) completely describe the parts, both visually and dimensionally;(b)show the parts in assembly;(3)identify all the parts; and (4)specify standard parts. The graphics and text information must be sufficiently complete and accurate to manufacture and assemble the product without error.共5 页第2 页Generally, a complete set of Working drawings for an assembly includes:(1) Detail drawings of each nonstandard parts.(2) An assembly or subassembly drawing showing all the standard and nonstandard parts in asingle drawing.(3) A bill of materials (BOM).(4) A title block.A detail drawing is a dimensioned, multiview drawing of a single part, describing the part’sshape, size, material, and surface roughness, in sufficient detail for the part to be manufactured based on the drawing alone. Detail drawings are produced from design sketches or extracted from 3-D computer models. They adhere strictly to ANSI standards and the standard for the specific company, for dimensioning, assigning part numbers, notes, tolerances, etc.In an assembly, standard parts such as threaded fasteners and bearings are not drawn as details, but are shown in the assembly view. Standard parts are not drawn as details because they are normally purchased, not manufactured, for assembly.For large assemblies or assembled with large parts, details are drawn on multiple sheets, and a separate sheet is used for the assembly view. If the assembly is simple or the parts are small, detail drawings for each part of an assembly can be placed on a single sheet.Multiple details on a sheet are usually drawn at the same scale. If different scales are used, they are clearly marked under each detail. Also, when more than one detail is placed on a sheet, the spacing between details is carefully planned, including leaving sufficient room for dimensions and notes.36. Based on the message, which of the follow is wrong?A) standard parts needn’t drawn as detailsB) for simple parts, sometimes needn’t to draw the detail working drawingC) for large assemblies details may drawn on multiple sheetsD) If different scales used in a single sheet, they should clearly marked under each detail37. Working drawings for an assembly MAY NOT includes: ?A) BOM B) title blockC) An assembly or subassembly drawing D) Detail drawings of all parts.38. For detail drawing, Which one of the following statement is not true ?A) should not has different scalesB) Detail drawing can produced from design sketches or extracted from 3-D computermodels.C) detail drawing is a dimensioned, multiview drawing of a single partD) should Adhere strictly to ANSI standards39. From the message we can infer ANSI is ?A) a set of law B) a set of notificationC) an Organization D) IEEE government40. The topic of the message is ?A) How to design working drawings.B) working drawing’s character and the key influence of how to draw a working drawingC) In the design process, what should be considered?D) What is working drawing订线TEXT 3One principle aim of kinematics is to create (design) the desired motions of the subjects’ mechanical parts and then mathematically compute the positions, velocities, and accelerations, which those will create on the parts. Since, for most earthbound mechanical systems, the mass remains essentially constant with time, defining the accelerations as a function of time then also defines the dynamic forces as a function of time. Stresses, in turn, will be a function of both applied and inertial (ma) forces. Since engineering design is charged with creating systems which will not fail during their expected service life, the goal is to keep stresses within acceptable limits for the materials chosen and the environmental conditions encountered. This obviously requires that all system forces be defined and kept within desired limits. In machinery, the largest forces encountered are often those due to the dynamics of the machine itself. These dynamic forces are proportional to acceleration, which brings us back to kinematics, the foundation of mechanical design. Very basic and early decision in the design process involving kinematic principles can be crucial to the success of any mechanical design; a design which has poor kinematics will prove troublesome and perform badly.Any mechanical system can be classified according to the number of degree of freedom (DOF) which it processes. The system’s DOF is equal to the number of independent parameters which are needed to uniquely define its position in space at any instant of time.A rigid body free to move within a reference frame will, in the general case, has complex motion, which is a simultaneous combination of rotation and translation. In three-dimensional space, there may be rotation about any axis and also simultaneous translation which can be resolved into components along three axes. In a plane, or two-dimensional space, complex motion becomes a combination of simultaneous rotation about one axis (perpendicular to the plane) and also translation resolved into components along two axes in the plane41、Kinematics research focus on the following EXCEPT ______..A) accelerations B) forceC) positions, D) velocities42、From the passage that for the design engineer the first and most important is____A) the service life of a machine B) the materials chooseC) the degree of freedom D) kinematic principles43、An mechanical system has uniquely defined position in space at any instant of time that ___.A) It should has one DOFB) It should Has two DOFC) the DOF and the number of independent parameters should equalD) It should has any DOF as want44、which one of the following is NOT TRUE _______?.A) A rigid body free to move in three-dimensional space may rotation about any axisB) A rigid body free to move in three-dimensional space is a simultaneous combination of rotationand translationC) A rigid body free to move in three-dimensional space can be resolved into components alongthree axesD) A rigid body free to move in three-dimensional space may has complex motion45、The best title for this passage would be _______.A) kinematics B) dynamicsC) kinematics and dynamic D) the important of kinematics共5 页第3 页四、英译汉（共20分）1．将下列英语句子译成汉语（共5小题，每小题2分）（1）A perfectly rigid or inextensible link can exist only as a textbook type of model ofa real machine member.（2）Foundations should ensure the machine’s stiffness; shock absorption and isolation are secondary considerations.(3)Thus, if a mechanical component such as a spring is subjected to repetitive or cyclicalapplications of stress levels much lower than the ultimate strength, it will fracture after a large number of repetitions of this stress.（4）Interchangeability means that identical parts must be interchangeable, i.e., able to replace each other, whether during assembly or subsequent maintenance work; without the need for any fitting operations.（5）A gear can be gripped axially between a shoulder on the shaft and a spacer, with torque being transmitted through a key.2、将下段英语翻译成汉语（10分）As we look around us we see a world full of “things”, machines, devices, tools, things that we have designed, built, and used; things made of wood, metals, ceramics, and plastics. We know from experience that some things are better than others; they last longer, cost less, are quieter, or are ease to use. Ideally , however, every such item has been designed according to some set of “functional requirements” as perceived by the designers—that is, it hs been designed so as to answer the question, “exactly what function should it perform?” In the world of engineering, the major function frequently is to support some type of loading due to weight, inertia , pressure, etc.五、英译汉（共20分）1、将下列句子译成英语（共5小题，每小题2分）（1）一个或多个诸如齿轮，链轮，皮带轮和凸轮等类的构件通常借助于销、键、花键、卡环或其它装置连接到轴上。
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Stationary iterative methods are methods for solving a linear system of equations
where is a given matrix and is a given vector. Stationary iterative methods can be expressed in the simple form
where neither nor depends upon the iteration count . The four main stationary methods are the Jacobi method, Gauss-Seidel method, successive overrelaxation method (SOR), and symmetric successive overrelaxation method (SSOR).
The Jacobi method is based on solving for every variable locally with respect to the other variables; one iteration corresponds to solving for every variable once. It is easy to understand and implement, but convergence is slow.
The Gauss-Seidel method is similar to the Jacobi method except that it uses updated values as soon as they are available. It generally converges faster than the Jacobi method, although still relatively slowly.
The successive overrelaxation method can be derived from the Gauss-Seidel method by introducing an extrapolation parameter . This method can converge faster than Gauss-Seidel by an order of magnitude.
Finally, the symmetric successive overrelaxation method is useful as a preconditioner for nonstationary methods. However, it has no advantage over the successive overrelaxation method as a stand-alone iterative method.。