Multimomentum Maps in General Relativity

合集下载

多目标跟踪的多伯努利平滑方法

多目标跟踪的多伯努利平滑方法

多目标跟踪的多伯努利平滑方法1 引言多目标跟踪是一种智能多智能体系统,它可以预测一组潜在的目标,并在其生命周期中跟踪它们。

多目标跟踪算法是当今多智能体系统的重要组成部分。

他们的主要功能是数据收集、分析和跟踪,它们可以帮助用户在其决策和行为中了解和规划他们要追踪的目标。

有几种流行的多目标跟踪算法,包括卡尔曼滤波(KF)、线性卡尔曼滤波(LKF)和半监督离散空间平滑算法(BSS)。

多伯努利平滑 (MBMS) 方法是一种基于多目标跟踪算法的先进方法。

它专门用于预测各种类型和形式的潜在目标,并通过监督离散空间平滑(SSS)算法实现跟踪。

MBMS解决了卡尔曼滤波(KF)和线性卡尔曼滤波(LKF)所存在的问题,如高误差和低效率。

总而言之,MBMS可以提高目标跟踪的准确性和效率。

2 MBMS方法概述MBMS方法利用了监督离散空间平滑(SSS)算法来预测和跟踪潜在的复杂目标。

MBMS算法通过监督离散空间平滑(SSS)再抽象出精简的核心算法,以实现其中的平滑处理。

这使得MBMS算法能够动态估计和有效地跟踪目标的多个参数(如位置、速度和加速度等)。

它的主要特点是简便、高效且精确。

3 基于MBMS的混合滤波基于MBMS的混合滤波(MHF)是一种改进的多伯努利平滑(MBMS)方法,它可以有效地处理复杂的多目标运动模型,同时具有针对潜在目标噪声严重的情况的稳健性能。

MHF方法利用了已经检测到的目标来预测未知目标,并利用历史目标位置数据创建一个比较新的位置估计。

通过相互约束,MHF算法可以有效地控制目标的运动,从而减少不确定性。

4 结论多伯努利平滑(MBMS)是一种简单实用的多目标跟踪算法,可以有效地预测和跟踪潜在的复杂目标。

它的特点是简单、高效且精确,可以提高目标跟踪的准确性和效率。

此外,MHF算法是MBMS的改进,可以有效处理复杂的多目标运动模型,具有较好的稳健性能。

未来研究可能会在这两种方法中建立更加复杂的模型,以实现更高效的跟踪结果。

多目标投影寻踪评价模型的可视化界面

多目标投影寻踪评价模型的可视化界面
助评 价 的 物元 分析 法 和灰 色 关联 度法 。
由于各种 模型对评 价数 据所 提供 信息 的利 用率不 同 , 因此 评价结果提供 的信 息量 也有所 区别 。为 了使 评价结 论更 加趋
方案 1 为了尽可能减少 黄河下 游河道 的淤积 和塑造 中水 :
河槽 , 同时考虑 国民经 济发 展对黄 河水 资源 的需求 , 南水北 在
ct n A A—P C 已经得 到广 泛应用 。但 由于其数 学 建模 ai ,R G o P) 及编程 的复杂性 , 因此短 时间内很 难得 到一 个通 用性较 强的程
序 。笔 者 利 用 V sa B s . 程 语 言 “ , 成 了 一 个 可 执 i l ai 6 0编 u c 生
行 的“ ee 文件 , 而建 立模 型的可 视化 界面 , .x” 从 通过 黄河 水资 源配置方案选择 的实例应用 , 明该通 用软件 能够使得在 利用 表 同类模型进行评价分析时更加快速准确 、 观方便。 直
思路是 : ①以维 持黄 河健 康 生命 为 出 发点 ; 协调 好生 活 、 ② 生 产、 生态用水 的关系 ; ③上 、 下游统 筹兼顾 ; 中、 ④在配 置方案 中 包括引水和耗水指标 ; ⑤保证干支流主要 断面维持一定 的下泄
维普资讯
第2 9卷第 l 2期 20 07年 1 2月




Vo . 9. . 2 1 2 No 1 De .. 0 7 c 2 0
YELLOW RI VER
【 资源 】 水
多 目标 投 影 寻 踪 评 价 模 型 的 可 视 化 界 面
量 。现 以规划水平年 (0 0年 ) 22 黄河沿岸 各省( ) 区 二级 区套 省的 4种方案水资源配置表为算例 , 在对各 种方案进行评 价的 同时说明可视化界面计算软件 的运行过程 。 黄河水资源配置 的主要影 响 因素为 多年平 均人海 水量 和 各省 ( ) 区 的缺水 比例以及现状用水情况 。以 22 0 0年为配 置水 平年 , 下垫面条件变化减少 地表径 流量 1 5亿 m 。初 步拟订 以

MOMENTUM MAPPINGS

MOMENTUM MAPPINGS

MOMENTUM MAPPINGSPeter W.MichorThe momentum map is essentially due to Lie,[5],pp.300–343.The modern notion is due to Kostant[3],Souriau[9],and Kirillov[2].The setting for the moment mapping is a smooth symplectic manifold(M,ω) or even a Poisson manifold(M,P)with the Poisson bracket on functions{f,g}= P(d f,dg)(where P=ω−1:T∗M→T M is the Poisson tensor).To each func-tion f there is the associated Hamiltonian vectorfield H f=P(d f)∈X(M,P), where X(M,P)is the Lie algebra of all locally Hamiltonian vectorfields Y∈X(M) satisfying L Y P=0for the Lie derivative.Let(M,ω)be a symplectic manifold for some time.Then this can be subsumed into the following exact sequence of Lie algebra homomorphisms0→H0(M)→C∞(M)X−→X(M,ω)γ−→H1(M)→0,whereγ(Y)=[i Yω],the De Rham cohomology class of the contraction of Y into ω,and where the brackets not yet mentioned are all0.A Lie group G can act from the right on M byα:M×G→M in a way which respectsω,so that we get a homomorphismα′:g→X(M,ω),where g is the Lie algebra of G.(For a left action we get an anti homomorphism of Lie algebras). One can liftα′to a linear mapping j:g→C∞(M)ifγ◦α′=0;if not we replace g by its Lie subalgebra ker(γ◦α′)⊂g.The question is whether one can change j into an homomorphism of Lie algebras.The map g∋X,Y→{jX,jY}−j([X,Y]) then induces a Chevalley2-cocycle in H2(g,H0(M)).If it vanishes one can change j as desired.If not,the cocycle describes a central extension of g on which one may change j to a homomorphism of Lie algebras.In any case,even for a Poisson manifold,for a homomorphism of Lie algebras j:g→C∞(M)(or more generally,if j is just a linear mapping),byflipping coordinates we get a momentum mapping J of the g-actionα′from M into the dual g∗of the Lie algebra g,J:M→g∗, J(x),X =j(X)(x),H j(X)=α′(X),x∈M,X∈g, where , is the duality pairing.For a particle in Euclidean3-space and the rotation group acting on T∗R3this is just the angular momentum,hence its name.The momentum map is infinitesimally equivariant for the g-actions if j is a homomorphism of Lie algebras.It is a Poisson morphism for the canonical Poisson structure on g∗,whose symplectic leaves are 1991Mathematics Subject Classification.37J15,53D20,70H33.Typeset by A M S-T E X12PETER W.MICHORthe coadjoint orbits.The momentum map can be used to reduce the number of coordinates of the original mechanical problem,hence plays an important role in the theory of reductions of Hamiltonian systems.[6],[4]and[7]are convenient references,[7]has a large and updated bibliography.The momentum map has a strong tendency to have convex image,and is important for representation theory, see[2]and[8].Recently,there is also a proposal for a group-valued momentum mapping,see[1].References[1]Alekseev,A.;Malkin,A.;Meinrenken,E.,Lie group valued moment maps,J.Differ.Geom48(1998),445-495.[2]Kirillov,A.A.,Elements of the theory of representations,Springer-Verlag,Berlin,1976.[3]Kostant,B.,Orbits,symplectic structures,and representation theory,Proc.United States-Japan Semin.Diff.Geom.,Nippon Hyoronsha,Tokyo,1966,p.71.[4]Libermann,P.;Marle,C.M.,Symplectic geometry and analytic mechanics,D.Reidel,1987.[5]Lie,S.,Theorie der Transformationsgruppen.Zweiter Abschnitt.,Teubner,Leipzig,1890.[6]Marmo,G.;Saletan,E.;Simoni,A.;Vitale,B.,Dynamical systems.A differential geometricapproach to symmetry and reduction,Wiley-Interscience,Chichester etc.,1985.[7]Marsden,J;Ratiu,T.,Introduction to Mechanics and Symmetry,Springer-Verlag,New York,2nd ed.1999.[8]Neeb,Karl-Hermann,Holomorphy and convexity in Lie theory,de Gruyter,Berlin,1999.[9]Souriau,J.M.,Quantification g´e om´e trique,Comm.Math.Phys.1(1966),374–398.Institut f¨u r Mathematik,Universit¨a t Wien,Strudlhofgasse4,A-1090Wien,Aus-triaE-mail address:Peter.Michor@esi.ac.at。

斯仑贝谢所有测井曲线英文名称解释

斯仑贝谢所有测井曲线英文名称解释

斯仑贝谢所有测井曲线英文名称解释OCEAN DRILLING PROGRAMACRONYMS USED FOR WIRELINE SCHLUMBERGER TOOLS ACT Aluminum Clay ToolAMS Auxiliary Measurement SondeAPS Accelerator Porosity SondeARI Azimuthal Resistivity ImagerASI Array Sonic ImagerBGKT Vertical Seismic Profile ToolBHC Borehole Compensated Sonic ToolBHTV Borehole TeleviewerCBL Casing Bond LogCNT Compensated Neutron ToolDIT Dual Induction ToolDLL Dual LaterologDSI Dipole Sonic ImagerFMS Formation MicroScannerGHMT Geologic High Resolution Magnetic ToolGPIT General Purpose Inclinometer ToolGR Natural Gamma RayGST Induced Gamma Ray Spectrometry ToolHLDS Hostile Environment Lithodensity SondeHLDT Hostile Environment Lithodensity ToolHNGS Hostile Environment Gamma Ray SondeLDT Lithodensity ToolLSS Long Spacing Sonic ToolMCD Mechanical Caliper DeviceNGT Natural Gamma Ray Spectrometry ToolNMRT Nuclear Resonance Magnetic ToolQSST Inline Checkshot ToolSDT Digital Sonic ToolSGT Scintillation Gamma Ray ToolSUMT Susceptibility Magnetic ToolUBI Ultrasonic Borehole ImagerVSI Vertical Seismic ImagerWST Well Seismic ToolWST-3 3-Components Well Seismic ToolOCEAN DRILLING PROGRAMACRONYMS USED FOR LWD SCHLUMBERGER TOOLSADN Azimuthal Density-NeutronCDN Compensated Density-NeutronCDR Compensated Dual ResistivityISONIC Ideal Sonic-While-DrillingNMR Nuclear Magnetic ResonanceRAB Resistivity-at-the-BitOCEAN DRILLING PROGRAMACRONYMS USED FOR NON-SCHLUMBERGER SPECIALTY TOOLSMCS Multichannel Sonic ToolMGT Multisensor Gamma ToolSST Shear Sonic ToolTAP Temperature-Acceleration-Pressure ToolTLT Temperature Logging ToolOCEAN DRILLING PROGRAMACRONYMS AND UNITS USED FOR WIRELINE SCHLUMBERGER LOGSAFEC APS Far Detector Counts (cps)ANEC APS Near Detector Counts (cps)AX Acceleration X Axis (ft/s2)AY Acceleration Y Axis (ft/s2)AZ Acceleration Z Axis (ft/s2)AZIM Constant Azimuth for Deviation Correction (deg)APLC APS Near/Array Limestone Porosity Corrected (%)C1 FMS Caliper 1 (in)C2 FMS Caliper 2 (in)CALI Caliper (in)CFEC Corrected Far Epithermal Counts (cps)CFTC Corrected Far Thermal Counts (cps)CGR Computed (Th+K) Gamma Ray (API units)CHR2 Peak Coherence, Receiver Array, Upper DipoleCHRP Compressional Peak Coherence, Receiver Array, P&SCHRS Shear Peak Coherence, Receiver Array, P&SCHTP Compressional Peak Coherence, Transmitter Array, P&SCHTS Shear Peak Coherence, Transmitter Array, P&SCNEC Corrected Near Epithermal Counts (cps)CNTC Corrected Near Thermal Counts (cps)CS Cable Speed (m/hr)CVEL Compressional Velocity (km/s)DATN Discriminated Attenuation (db/m)DBI Discriminated Bond IndexDEVI Hole Deviation (degrees)DF Drilling Force (lbf)DIFF Difference Between MEAN and MEDIAN in Delta-Time Proc. (microsec/ft) DRH HLDS Bulk Density Correction (g/cm3)DRHO Bulk Density Correction (g/cm3)DT Short Spacing Delta-Time (10'-8' spacing; microsec/ft)DT1 Delta-Time Shear, Lower Dipole (microsec/ft)DT2 Delta-Time Shear, Upper Dipole (microsec/ft)DT4P Delta- Time Compressional, P&S (microsec/ft)DT4S Delta- Time Shear, P&S (microsec/ft))DT1R Delta- Time Shear, Receiver Array, Lower Dipole (microsec/ft)DT2R Delta- Time Shear, Receiver Array, Upper Dipole (microsec/ft)DT1T Delta-Time Shear, Transmitter Array, Lower Dipole (microsec/ft)DT2T Delta-Time Shear, Transmitter Array, Upper Dipole (microsec/ft)DTCO Delta- Time Compressional (microsec/ft)DTL Long Spacing Delta-Time (12'-10' spacing; microsec/ft)DTLF Long Spacing Delta-Time (12'-10' spacing; microsec/ft)DTLN Short Spacing Delta-Time (10'-8' spacing; microsec/ftDTRP Delta-Time Compressional, Receiver Array, P&S (microsec/ft)DTRS Delta-Time Shear, Receiver Array, P&S (microsec/ft)DTSM Delta-Time Shear (microsec/ft)DTST Delta-Time Stoneley (microsec/ft)DTTP Delta-Time Compressional, Transmitter Array, P&S (microsec/ft)DTTS Delta-Time Shear, Transmitter Array, P&S (microsec/ft)ECGR Environmentally Corrected Gamma Ray (API units)EHGR Environmentally Corrected High Resolution Gamma Ray (API units) ENPH Epithermal Neutron Porosity (%)ENRA Epithermal Neutron RatioETIM Elapsed Time (sec)FINC Magnetic Field Inclination (degrees)FNOR Magnetic Field Total Moment (oersted)FX Magnetic Field on X Axis (oersted)FY Magnetic Field on Y Axis (oersted)FZ Magnetic Field on Z Axis (oersted)GR Natural Gamma Ray (API units)HALC High Res. Near/Array Limestone Porosity Corrected (%)HAZI Hole Azimuth (degrees)HBDC High Res. Bulk Density Correction (g/cm3)HBHK HNGS Borehole Potassium (%)HCFT High Resolution Corrected Far Thermal Counts (cps)HCGR HNGS Computed Gamma Ray (API units)HCNT High Resolution Corrected Near Thermal Counts (cps)HDEB High Res. Enhanced Bulk Density (g/cm3)HDRH High Resolution Density Correction (g/cm3)HFEC High Res. Far Detector Counts (cps)HFK HNGS Formation Potassium (%)HFLC High Res. Near/Far Limestone Porosity Corrected (%)HEGR Environmentally Corrected High Resolution Natural Gamma Ray (API units) HGR High Resolution Natural Gamma Ray (API units)HLCA High Res. Caliper (inHLEF High Res. Long-spaced Photoelectric Effect (barns/e-)HNEC High Res. Near Detector Counts (cps)HNPO High Resolution Enhanced Thermal Nutron Porosity (%)HNRH High Resolution Bulk Density (g/cm3)HPEF High Resolution Photoelectric Effect (barns/e-)HRHO High Resolution Bulk Density (g/cm3)HROM High Res. Corrected Bulk Density (g/cm3)HSGR HNGS Standard (total) Gamma Ray (API units)HSIG High Res. Formation Capture Cross Section (capture units) HSTO High Res. Computed Standoff (in)HTHO HNGS Thorium (ppm)HTNP High Resolution Thermal Neutron Porosity (%)HURA HNGS Uranium (ppm)IDPH Phasor Deep Induction (ohmm)IIR Iron Indicator Ratio [CFE/(CCA+CSI)]ILD Deep Resistivity (ohmm)ILM Medium Resistivity (ohmm)IMPH Phasor Medium Induction (ohmm)ITT Integrated Transit Time (s)LCAL HLDS Caliper (in)LIR Lithology Indicator Ratio [CSI/(CCA+CSI)]LLD Laterolog Deep (ohmm)LLS Laterolog Shallow (ohmm)LTT1 Transit Time (10'; microsec)LTT2 Transit Time (8'; microsec)LTT3 Transit Time (12'; microsec)LTT4 Transit Time (10'; microsec)MAGB Earth's Magnetic Field (nTes)MAGC Earth Conductivity (ppm)MAGS Magnetic Susceptibility (ppm)MEDIAN Median Delta-T Recomputed (microsec/ft)MEAN Mean Delta-T Recomputed (microsec/ft)NATN Near Pseudo-Attenuation (db/m)NMST Magnetometer Temperature (degC)NMSV Magnetometer Signal Level (V)NPHI Neutron Porosity (%)NRHB LDS Bulk Density (g/cm3)P1AZ Pad 1 Azimuth (degrees)PEF Photoelectric Effect (barns/e-)PEFL LDS Long-spaced Photoelectric Effect (barns/e-)PIR Porosity Indicator Ratio [CHY/(CCA+CSI)]POTA Potassium (%)RB Pad 1 Relative Bearing (degrees)RHL LDS Long-spaced Bulk Density (g/cm3)RHOB Bulk Density (g/cm3)RHOM HLDS Corrected Bulk Density (g/cm3)RMGS Low Resolution Susceptibility (ppm)SFLU Spherically Focused Log (ohmm)SGR Total Gamma Ray (API units)SIGF APS Formation Capture Cross Section (capture units)SP Spontaneous Potential (mV)STOF APS Computed Standoff (in)SURT Receiver Coil Temperature (degC)SVEL Shear Velocity (km/s)SXRT NMRS differential Temperature (degC)TENS Tension (lb)THOR Thorium (ppm)TNRA Thermal Neutron RatioTT1 Transit Time (10' spacing; microsec)TT2 Transit Time (8' spacing; microsec)TT3 Transit Time (12' spacing; microsec)TT4 Transit Time (10' spacing; microsec)URAN Uranium (ppm)V4P Compressional Velocity, from DT4P (P&S; km/s)V4S Shear Velocity, from DT4S (P&S; km/s)VELP Compressional Velocity (processed from waveforms; km/s)VELS Shear Velocity (processed from waveforms; km/s)VP1 Compressional Velocity, from DT, DTLN, or MEAN (km/s)VP2 Compressional Velocity, from DTL, DTLF, or MEDIAN (km/s)VCO Compressional Velocity, from DTCO (km/s)VS Shear Velocity, from DTSM (km/s)VST Stonely Velocity, from DTST km/s)VS1 Shear Velocity, from DT1 (Lower Dipole; km/s)VS2 Shear Velocity, from DT2 (Upper Dipole; km/s)VRP Compressional Velocity, from DTRP (Receiver Array, P&S; km/s) VRS Shear Velocity, from DTRS (Receiver Array, P&S; km/s)VS1R Shear Velocity, from DT1R (Receiver Array, Lower Dipole; km/s) VS2R Shear Velocity, from DT2R (Receiver Array, Upper Dipole; km/s) VS1T Shear Velocity, from DT1T (Transmitter Array, Lower Dipole; km/s) VS2T Shear Velocity, from DT2T (Transmitter Array, Upper Dipole; km/s) VTP Compressional Velocity, from DTTP (Transmitter Array, P&S; km/s) VTS Shear Velocity, from DTTS (Transmitter Array, P&S; km/s)#POINTS Number of Transmitter-Receiver Pairs Used in Sonic Processing W1NG NGT Window 1 counts (cps)W2NG NGT Window 2 counts (cps)W3NG NGT Window 3 counts (cps)W4NG NGT Window 4 counts (cps)W5NG NGT Window 5 counts (cps)OCEAN DRILLING PROGRAMACRONYMS AND UNITS USED FOR LWD SCHLUMBERGER LOGSAT1F Attenuation Resistivity (1 ft resolution; ohmm)AT3F Attenuation Resistivity (3 ft resolution; ohmm)AT4F Attenuation Resistivity (4 ft resolution; ohmm)AT5F Attenuation Resistivity (5 ft resolution; ohmm)ATR Attenuation Resistivity (deep; ohmm)BFV Bound Fluid Volume (%)B1TM RAB Shallow Resistivity Time after Bit (s)B2TM RAB Medium Resistivity Time after Bit (s)B3TM RAB Deep Resistivity Time after Bit (s)BDAV Deep Resistivity Average (ohmm)BMAV Medium Resistivity Average (ohmm)BSAV Shallow Resistivity Average (ohmm)CGR Computed (Th+K) Gamma Ray (API units)DCAL Differential Caliper (in)DROR Correction for CDN rotational density (g/cm3).DRRT Correction for ADN rotational density (g/cm3).DTAB AND or CDN Density Time after Bit (hr)FFV Free Fluid Volume (%)GR Gamma Ray (API Units)GR7 Sum Gamma Ray Windows GRW7+GRW8+GRW9-Equivalent to Wireline NGT window 5 (cps) GRW3 Gamma Ray Window 3 counts (cps)-Equivalent to Wireline NGT window 1GRW4 Gamma Ray Window 4 counts (cps)-Equivalent to Wireline NGT window 2GRW5 Gamma Ray Window 5 counts (cps)-Equivalent to Wireline NGT window 3GRW6 Gamma Ray Window 6 counts (cps)-Equivalent to Wireline NGT window 4GRW7 Gamma Ray Window 7 counts (cps)GRW8 Gamma Ray Window 8 counts (cps)GRW9 Gamma Ray Window 9 counts (cps)GTIM CDR Gamma Ray Time after Bit (s)GRTK RAB Gamma Ray Time after Bit (s)HEF1 Far He Bank 1 counts (cps)HEF2 Far He Bank 2 counts (cps)HEF3 Far He Bank 3 counts (cps)HEF4 Far He Bank 4 counts (cps)HEN1 Near He Bank 1 counts (cps)HEN2 Near He Bank 2 counts (cps)HEN3 Near He Bank 3 counts (cps)HEN4 Near He Bank 4 counts (cps)MRP Magnetic Resonance PorosityNTAB ADN or CDN Neutron Time after Bit (hr)PEF Photoelectric Effect (barns/e-)POTA Potassium (%) ROPE Rate of Penetration (ft/hr)PS1F Phase Shift Resistivity (1 ft resolution; ohmm)PS2F Phase Shift Resistivity (2 ft resolution; ohmm)PS3F Phase Shift Resistivity (3 ft resolution; ohmm)PS5F Phase Shift Resistivity (5 ft resolution; ohmm)PSR Phase Shift Resistivity (shallow; ohmm)RBIT Bit Resistivity (ohmm)RBTM RAB Resistivity Time After Bit (s)RING Ring Resistivity (ohmm)ROMT Max. Density Total (g/cm3) from rotational processing ROP Rate of Penetration (m/hr)ROP1 Rate of Penetration, average over last 1 ft (m/hr).ROP5 Rate of Penetration, average over last 5 ft (m/hr)ROPE Rate of Penetration, averaged over last 5 ft (ft/hr)RPM RAB Tool Rotation Speed (rpm)RTIM CDR or RAB Resistivity Time after Bit (hr)SGR Total Gamma Ray (API units)T2 T2 Distribution (%)T2LM T2 Logarithmic Mean (ms)THOR Thorium (ppm)TNPH Thermal Neutron Porosity (%)TNRA Thermal RatioURAN Uranium (ppm)OCEAN DRILLING PROGRAMADDITIONAL ACRONYMS AND UNITS(PROCESSED LOGS FROM GEOCHEMICAL TOOL STRING)AL2O3 Computed Al2O3 (dry weight %)AL2O3MIN Computed Al2O3 Standard Deviation (dry weight %) AL2O3MAX Computed Al2O3 Standard Deviation (dry weight %) CAO Computed CaO (dry weight %)CAOMIN Computed CaO Standard Deviation (dry weight %) CAOMAX Computed CaO Standard Deviation (dry weight %) CACO3 Computed CaCO3 (dry weight %)CACO3MIN Computed CaCO3 Standard Deviation (dry weight %) CACO3MAX Computed CaCO3 Standard Deviation (dry weight %) CCA Calcium Yield (decimal fraction)CCHL Chlorine Yield (decimal fraction)CFE Iron Yield (decimal fraction)CGD Gadolinium Yield (decimal fraction)CHY Hydrogen Yield (decimal fraction)CK Potassium Yield (decimal fraction)CSI Silicon Yield (decimal fraction)CSIG Capture Cross Section (capture units)CSUL Sulfur Yield (decimal fraction)CTB Background Yield (decimal fraction)CTI Titanium Yield (decimal fraction)FACT Quality Control CurveFEO Computed FeO (dry weight %)FEOMIN Computed FeO Standard Deviation (dry weight %) FEOMAX Computed FeO Standard Deviation (dry weight %) FEO* Computed FeO* (dry weight %)FEO*MIN Computed FeO* Standard Deviation (dry weight %) FEO*MAX Computed FeO* Standard Deviation (dry weight %) FE2O3 Computed Fe2O3 (dry weight %)FE2O3MIN Computed Fe2O3 Standard Deviation (dry weight %) FE2O3MAX Computed Fe2O3 Standard Deviation (dry weight %) GD Computed Gadolinium (dry weight %)GDMIN Computed Gadolinium Standard Deviation (dry weight %) GDMAX Computed Gadolinium Standard Deviation (dry weight %) K2O Computed K2O (dry weight %)K2OMIN Computed K2O Standard Deviation (dry weight %)K2OMAX Computed K2O Standard Deviation (dry weight %) MGO Computed MgO (dry weight %)MGOMIN Computed MgO Standard Deviation (dry weight %) MGOMAX Computed MgO Standard Deviation (dry weight %)S Computed Sulfur (dry weight %)SMIN Computed Sulfur Standard Deviation (dry weight %) SMAX Computed Sulfur Standard Deviation (dry weight %)SIO2 Computed SiO2 (dry weight %)SIO2MIN Computed SiO2 Standard Deviation (dry weight %) SIO2MAX Computed SiO2 Standard Deviation (dry weight %) THORMIN Computed Thorium Standard Deviation (ppm) THORMAX Computed Thorium Standard Deviation (ppm)TIO2 Computed TiO2 (dry weight %)TIO2MIN Computed TiO2 Standard Deviation (dry weight %) TIO2MAX Computed TiO2 Standard Deviation (dry weight %) URANMIN Computed Uranium Standard Deviation (ppm) URANMAX Computed Uranium Standard Deviation (ppm) VARCA Variable CaCO3/CaO calcium carbonate/oxide factor。

Fluent 菜单命令

Fluent 菜单命令

Grid Array ArrayModels 模型 : solver 解算器Pressure based 基于压力 density based 基于密度implicit 隐式, explicit 显示Space 空间:2D,axisymmetric(转动轴),axisymmetric swirl (漩涡转动轴);Time时间 :steady 定常,unsteady 非定常Velocity formulation 制定速度:absolute绝对的; relative 相对的Gradient option 梯度选择: 以单元作基础;以节点作基础;以单元作梯度的最小正方形。

Porous formulation 多孔的制定:superticial velocity 表面速度;physical velocity物理速度;Name 定义物质的名称 chemical formula 化学反应式 material type 物质类型(液体,固体) Fluent fluid materials 流动的物质 mixture 混合物 order materials by 根据什么物质(名称/化学反应式)Fluent database 流体数据库 user ‐defined database 用户自定义数据库 Propertles 物质性质 从上往下 分别是 密度 比热容 导热系数 粘滞系数操作压力操作压力设置:operating pressure操作压力 reference pressure location 参考压力位置gravity 重力,地心引力gravitational Acceleration 重力加速度operating temperature 操作温度variable‐density parameters 可变密度的参数specified operating density 确切的操作密度边界条件设置定于流体Zone name区域名 material name 物质名 edit 编辑Porous zone 多空区域 laminar zone 薄层或者层状区域 source terms (源项?)Fixed values 固定值motion 运动rotation‐axis origin旋转轴原点Rotation‐axis direction 旋转轴方向Motion type 运动类型 : stationary静止的; moving reference frame 移动参考框架;Moving mesh 移动网格Porous zone 多孔区Reaction 反应Source terms (源项)Fixed values 固定值速度入口(velocity‐inlet)Momentum 动量? thermal 温度 radiation 辐射 species 种类DPM DPM模型(可用于模拟颗粒轨迹) multipahse 多项流UDS(User define scalar 是使用fluent求解额外变量的方法)Velocity specification method 速度规范方法 : magnitude,normal to boundary 速度大小,速度垂直于边界;magnitude and direction 大小和方向;components 速度组成? Reference frame 参考系:absolute绝对的;Relative to adjacent cell zone 相对于邻近的单元区Velocity magnitude 速度的大小Turbulence 湍流Specification method 规范方法k and epsilon K‐E方程:1 Turbulent kinetic energy湍流动能;2 turbulent dissipation rate 湍流耗散率Intensity and length scale 强度和尺寸 : 1湍流强度 2 湍流尺度=0.07L(L为水力半径) intensity and viscosity rate强度和粘度率:1湍流强度2湍流年度率intensity and hydraulic diameter强度与水力直径:1湍流强度;2水力直径压力入口(pressure‐inlet)Gauge total pressure 总压 supersonic/initial gauge pressure 超音速/初始 表压 constant常数direction specification method 方向规范方法 :1direction vector方向矢量;2 normal to boundary 垂直于边界质量入口(mass‐flow‐inlet)Mass flow specification method 质量流量规范方法 :1 mass flow rate 质量流量;2 mass Flux 质量通量 3mass flux with average mass flux 质量通量的平均通量supersonic/initial gauge pressure 超音速/初始 表压direction specification method 方向规范方法 :1direction vector方向矢量;2 normal to boundary 垂直于边界Reference frame 参考系:absolute绝对的;Relative to adjacent cell zone 相对于邻近的单元区压力出口(pressure‐outlet)Gauge pressure表压backflow direction specification method 回流方向规范方法:1direction vector方向矢量;2 normal to boundary 垂直于边界 ;3 from neighboring cell 邻近单元Radial equilibrium pressure distribution 径向平衡压力分布Target mass flow rate 质量流量指向压力远程(pressure‐far‐field)Mach number 马赫数 x‐component of flow direction X分量的流动方向自由出流 (outlet)Flow rate weighting 流量比重进口通风( inlet vent)Loss coeffcient 损耗系数 1 constant 常数;2 piecewise‐linear分段线性;3piecewise‐polynomial 分段多项式;4 polynomial 多项式EditDefine 定义 in terms of 在一下方面 normal‐velocity 正常速度 coefficients系数进口风扇(intake Fan)Pressure jump 压力跃 1 constant 常数;2 piecewise‐linear分段线性;3piecewise‐polynomial 分段多项式;4 polynomial 多项式排气扇(exhaust fan)对称边界(symmetry)周期性边界(periodic)固壁边界(wall)adjicent cell zone相邻的单元区Wall motion 室壁运动 :stationary wall 固定墙Shear condition 剪切条件 : no slip 无滑 ;specified shear 指定的剪切;specularity coefficients 镜面放射系数 marangoni stress 马兰格尼压力?Wall roughness 壁面粗糙度:roughness height 粗糙高度 roughness constant粗糙常数Moving wall 移动墙壁Translational 平移 rotational 转动 components 组成Solve/controls/solutionEquations 方程 under‐relaxation factors 松弛因子: body forces 体积力 Momentum动量 turbulent kinetic energy 湍流动能turbulent dissipation rate湍流耗散率 Turbulent viscosity 湍流粘度 energy 能量Pressure‐velocity coupling 压力速度耦合: simple ,simplec,plot和coupled是4种不同的算法。

Fractional Hamiltonian Monodromy from a Gauss-Manin Monodromy

Fractional Hamiltonian Monodromy from a Gauss-Manin Monodromy
FRACTIONAL HAMILTONIAN MONODROMY FROM A GAUSS-MANIN MONODROMY
arXiv:0709.2765v1 [math-ph] 18 Sep 2007
ˇ C ´ 2 ,M. PELLETIER2 ,A. JEBRANE2 ,H. R. JAUSLIN1 D. SUGNY1 ,P. MARDESI
1. Introduction We consider an integrable system on a four dimensional symplectic manifold defined by an energy-momentum map. For a proper map, the Liouville-Arnold theorem allows to foliate the phase space by tori or a disjoint union of tori over the regular values of the image of the map. Hamiltonian monodromy is the monodromy of this fibration [Dui80, CB97]. The word Hamiltonian is added to distinguish this monodromy from the Gauss-Manin monodromy of Riemann surfaces which is also used in this paper. A non trivial monodromy can be expected if the set of regular values of the image of the energy-momentum map is not simply connected. Hamiltonian monodromy has profound implications both in classical and quantum mechanics [Ngoc99] since it is the simplest topological obstruction to the existence of global action-angle variables [Dui80] and thus of global good quantum numbers [Ngoc99]. The phenomenon of Hamiltonian monodromy has been exhibited in a large variety of physical systems both in classical and quantum mechanics [AKE04, SC00, KR03, CWT99, SZ99, WJD03, GCSZ04, EJS04]. The presence of non-trivial monodromy in energy-momentum maps with isolated singularities of focus-focus type is now well-established. The non-trivial monodromy in the spherical pendulum is due to this singularity [Dui80, CB97]. Recently, the definition of Hamiltonian monodromy has been extended to characterize not only isolated singularities but also some types of non-isolated singularities, leading to the concept of Fractional Hamiltonian Monodromy [NSZ02, NSZ06, Efs04, ECS07]. More precisely, one considers an energy-momentum map with a 1-dimensional set C of weak critical values defined by the property that each point of this set lifts to a particular type of singular torus, a curled torus, i.e. for the simplest case two

多目标轮廓Mumford-Shah水平集提取

多目标轮廓Mumford-Shah水平集提取
ta to s p e e td.F rt h r d e tv co ed wa o i e t r ldie to ft e c r e sb u d . rc in wa r s ne is ,te g a in e t rf l sc mb n d wi noma r cin o h u v sa o n a i h r b ta t d f l s,S st e e a e a b — ie to lg o ti e oma l o fed wh c a rv ci e c n- y a sr ce e d i O a o g n r t idr cina e merc d f r b e f w l ih c n d i e a tv o l i tu se o vn o r s t e b un a y fo i sde o u sd d e . F rh r r o r v li g twa d h o d r r m n i r o ti e e g s u t e mo e,t e d srb td i fr ai n o h h iti u e n o m to f t e i g u d b e ta r a e ou in e e g .Th smeh d c n s l e p o l mst a rs e r a e e g n o ma ma e wo l e lf sa e v l to n r y i t o a ov r b e h ta iewh n a e n r y i fr — t n i o tb c us o a e merc i fr ai n intc nsd r d,o e o oo ia tucu es o l o e c a g d i sl s e a e lc lg o ti n o o m to s o i e e rwh n tp lgc lsr t r h u d n tb h n e b c u e t e g a in e t rfed i rh g na t o ma ie t n.Th n t e lv l s tf n to s mo i e O e a s h r d e tv co l s o o o lwih n r ld r ci i t o e h e e e u ci n wa d f d S i

代数英语

代数英语

(0,2) 插值||(0,2) interpolation0#||zero-sharp; 读作零井或零开。

0+||zero-dagger; 读作零正。

1-因子||1-factor3-流形||3-manifold; 又称“三维流形”。

AIC准则||AIC criterion, Akaike information criterionAp 权||Ap-weightA稳定性||A-stability, absolute stabilityA最优设计||A-optimal designBCH 码||BCH code, Bose-Chaudhuri-Hocquenghem codeBIC准则||BIC criterion, Bayesian modification of the AICBMOA函数||analytic function of bounded mean oscillation; 全称“有界平均振动解析函数”。

BMO鞅||BMO martingaleBSD猜想||Birch and Swinnerton-Dyer conjecture; 全称“伯奇与斯温纳顿-戴尔猜想”。

B样条||B-splineC*代数||C*-algebra; 读作“C星代数”。

C0 类函数||function of class C0; 又称“连续函数类”。

CA T准则||CAT criterion, criterion for autoregressiveCM域||CM fieldCN 群||CN-groupCW 复形的同调||homology of CW complexCW复形||CW complexCW复形的同伦群||homotopy group of CW complexesCW剖分||CW decompositionCn 类函数||function of class Cn; 又称“n次连续可微函数类”。

Cp统计量||Cp-statisticC。

稠密点云

稠密点云

获得稠密点云的方法主要分为两类:一类是从物体的剪影重建物体的大致轮廓(shape From silhouette[ˌsɪluˈet])。

SFS的基本思想是,物体所在的区域必然包含在这些视锥的交集内部。

每个相机与物体在此图像上的轮廓形成视锥,以这种方式形成的模型称为可视外壳。

它是物体外轮廓的近视描述。

可视外壳重建的常用方法是EPVH(Exact Polyhedral Visual Hulls)算法。

另一类是从目标所在区域的成像属性出发,利用光度一致性约束,将稀疏特征点周围的一些区域恢复出三维信息,实质上是运动信息结构化的稠密版本。

常用的方法是采用基于面片的多视图三维重建PMVS (Patched-based Multe-View Stereo)算法。

第二类方法比第一类方法有个更高的重建精度。

PMVS算法:多视图立体视觉:(Multiview Stereo,MVS)配准和重建是基于图像三维重建的核心步骤。

MVS算法可以根据隐含的对象模型被划为4个类型:(1)基于体素的方法:需要一个包含场景的包围盒,它的精度被体素网格的分辨率所限制。

(2)基于可变多边形网格的方法:需要一个比较好的起始点。

(3)基于多深度图像的方法:非常灵活,但需要将深度图像融入三维模型中。

(4)基于面片的方法:以小块集合的形式展示场景。

这种方法简单有效,并且能够符合基于点的绘制技术的视觉要求,但是需要一个后续的处理步骤将块的集合转化成网格模型。

PMVS算法属于第四类。

该算法准确,简单,高效,能够自动检测和忽略外部点和障碍点,输出具有方向的小矩形面片密集集合。

对纹理覆盖不足、凹陷和高区率的区域也有较好的重建效果。

一个面片p本质上是曲面的局部切平面逼近。

面片p是一个带方向的矩形。

点云配准:对于两个点云数据来说,配准的目的就在于找到一个最优的几何变换使得两个点云数据在同一个坐标系下最大程度的对齐融合。

点云几何计算获取方法:法向及曲面变分计算:基于局部表面拟合点云特征提取:(1)基于投影点均匀性的方法(2)基于度量函数的方法(3)基于距离比值的方法通过采样点到领域重心点与采样点到领域最远点的距离比值来识别特征点的算法。

一类两个边界带谱参数的正则Sturm-Liouville算子的基本解的渐近分析

一类两个边界带谱参数的正则Sturm-Liouville算子的基本解的渐近分析
ope a orA Sde i d i uia e H ibe ts c s h t tt na y i f s h r blm e e ta f r e r t W fne n a s t bl l r pa e H uc ha he a l ss o uc p o e w r r ns o m d i o t os pe a orA . T h s m pt i or u a u nt h eofo r t ea y otc f m l soff nda e t ls u i her gulr St r Liuvi eop— m n a ol tonsoft e a u m- o l l e a ora nt g a u to w e e a s i e r t nd i e r lf nc in r lo g v n. Ke r s:s ecr lpa a e e ; ta m iso c nd tons r gu a y wo d p t a— r m t r r ns s in o ii ; e l r;f da e a o uton; a y pt tcf m u a un m nt ls l i s m o i or l e
中 图分 类 号 : 7 . O1 5 3 文献标识码 : A
As m p o i a y i f a Cl s f Re u a t r - o v ie O p r t r y t tc An l ss o a s o g l r S u m - Li u il e a o s w ih S e t a — r m e e s i t p c r lpa a t r n Two Bo nd r n ii ns u a y Co d to
摘 要 :研 究 了一 类 具 有 转 换 条 件 且 两 个 边 界 条 件 中带 谱 参 数 的 正则 Sum— iu ie问题 . 用 常 数 变 易 法 导 tr Lo vl l 应 出 Su m Lo vl 初 值 问 题 的一 对 线 性 无 关 的 基 本 解 的表 达 式 , 后 将 该 问题 的 基 本 解 的 渐 近 分 析 , 化 为 tr — iu ie l 然 转 考 虑 定 义在 适 当 的 Hi et 间 H 中 的 一 个 线 性 自伴 算 子 A 的 基 本 解 的 渐 近 分 析 , 推 导 出 该 正 则 的 l r空 b 并 Sum- iu ie 子 A 的 基 本 解 的渐 近 式 和 整 函 数 的 渐 近 式 . tr Lo vl 算 l 关 键 词 : 参 数 ;转 换 条 件 ;正则 ; 本 解 ; 近 式 谱 基 渐

聚类算法英文专业术语

聚类算法英文专业术语

聚类算法英文专业术语1. 聚类 (Clustering)2. 距离度量 (Distance Metric)3. 相似度度量 (Similarity Metric)4. 皮尔逊相关系数 (Pearson Correlation Coefficient)5. 欧几里得距离 (Euclidean Distance)6. 曼哈顿距离 (Manhattan Distance)7. 切比雪夫距离 (Chebyshev Distance)8. 余弦相似度 (Cosine Similarity)9. 层次聚类 (Hierarchical Clustering)10. 分层聚类 (Divisive Clustering)11. 凝聚聚类 (Agglomerative Clustering)12. K均值聚类 (K-Means Clustering)13. 高斯混合模型聚类 (Gaussian Mixture Model Clustering)14. 密度聚类 (Density-Based Clustering)15. DBSCAN (Density-Based Spatial Clustering of Applications with Noise)16. OPTICS (Ordering Points To Identify the Clustering Structure)17. Mean Shift18. 聚类评估指标 (Clustering Evaluation Metrics)19. 轮廓系数 (Silhouette Coefficient)20. Calinski-Harabasz指数 (Calinski-Harabasz Index)21. Davies-Bouldin指数 (Davies-Bouldin Index)22. 聚类中心 (Cluster Center)23. 聚类半径 (Cluster Radius)24. 噪声点 (Noise Point)25. 簇内差异 (Within-Cluster Variation)26. 簇间差异 (Between-Cluster Variation)。

点云聚类 马氏距离

点云聚类 马氏距离

点云聚类与马氏距离1. 引言点云聚类是计算机视觉和机器学习领域中的一个重要任务,它旨在将大规模的点云数据集划分为若干个具有相似特征的子集。

点云数据通常是由三维空间中的离散点组成,例如激光雷达或摄像头采集到的数据。

而马氏距离是一种常用的距离度量方法,它考虑了数据之间的协方差结构,能够更好地反映数据之间的相关性。

本文将介绍点云聚类任务以及如何使用马氏距离进行点云聚类。

2. 点云聚类2.1 点云表示在进行点云聚类之前,首先需要对点云进行合适的表示。

一种常见的表示方法是使用坐标向量来表示每个点,在三维空间中,一个点可以由其x、y和z坐标构成。

因此,一个包含N个点的点云可以表示为一个N×3维度的矩阵。

2.2 点云聚类算法目前,有许多不同的算法可用于进行点云聚类,其中一些常用的算法包括K-means、DBSCAN和Mean Shift等。

这些算法通常根据点之间的距离或密度来进行聚类。

在本文中,我们将重点介绍基于马氏距离的点云聚类方法。

3. 马氏距离3.1 距离度量在机器学习和模式识别领域,距离度量是一项重要的任务。

它用于衡量数据之间的相似性或差异性。

马氏距离是一种常用的距离度量方法,它考虑了数据之间的协方差结构,能够更好地反映数据之间的相关性。

3.2 马氏距离计算给定两个向量x和y,它们分别表示两个点在特征空间中的位置。

马氏距离可以通过以下公式计算:d(x,y)=√(x−y)T C−1(x−y)其中,C是协方差矩阵。

协方差矩阵描述了数据中各个特征变量之间的关系。

通过使用协方差矩阵来计算马氏距离,可以考虑数据之间的相关性,使得聚类结果更加准确。

3.3 马氏距离在点云聚类中的应用在点云聚类中,可以使用马氏距离来度量点之间的相似性。

具体而言,对于给定的两个点x和y,在计算马氏距离之前,需要先计算它们之间的协方差矩阵C。

然后,可以使用上述公式计算它们之间的马氏距离。

4. 基于马氏距离的点云聚类算法基于马氏距离的点云聚类算法可以分为以下几个步骤:4.1 数据预处理首先,需要对原始的点云数据进行预处理。

马头星云 窄带 参数

马头星云 窄带 参数

马头星云窄带参数The Horsehead Nebula is a dark nebula located in the Orion constellation. It is a beautiful and captivating space formation that has intrigued astronomers and space enthusiasts for many years. Known for its distinctive shape that resembles the head of a horse, the Horsehead Nebula is a popular target for both amateur and professional astronomers.马头星云是位于猎户座的暗星云。

它是一个美丽而引人入胜的空间形态,多年来一直引起天文学家和太空爱好者的兴趣。

马头星云因其独特的形状,宛如一匹马的头部,是业余和专业天文学家的热门目标。

One of the most fascinating aspects of the Horsehead Nebula is its narrowband parameters. Narrowband imaging allows astronomers to focus on specific wavelengths of light, such as hydrogen-alpha and sulfur II, which helps to reveal intricate details of the nebula that may not be visible in broad-spectrum images. By using narrowband filters, astronomers can capture stunning images of the Horsehead Nebula that showcase its intricate structure and beauty.马头星云最令人着迷的一个方面是其窄带参数。

思维导图的8种基本类型

思维导图的8种基本类型

思维导图的8种基本类型思维导图有⼋种基本款。

都体现了基础的思维框架。

但是每种图都能有⽆限的延伸,甚⾄不同种图可以结合起来⼀起⽤,也可以变得⾮常复杂。

思维导图,英⽂叫 Mind Map,也有称 Thinking Map 的,简单来说,就是借助图表来分析问题、理清思路。

实际上,能帮助理清思维的图表都可以叫做思维图。

思维图能帮助孩⼦学习各种知识,基本上哪⾥都能⽤。

常见的思维图有这⼋种:Circle Maps - Defining in Context 圆圈图,定义⼀件事Bubble Maps -Describing Qualities ⽓泡图,描述事物性质和特征Double Bubble Maps - Comparing and Contrasting 双重⽓泡图,⽐较和对照Tree Map -Classifying 树状图,分类Flow Maps -Sequencing 流程图,次序Multi Flow Maps - Cause and Effect 多重流程图,因果关系Brace Maps -Part-Whole 括号图,局部和整体Bridge Maps -Seeing Analogies 桥状图,类⽐1、圆圈图,定义⼀件事Circle Maps - Defining in ContextCircle map 主要⽤于把⼀个主题展开来,联想或描述细节。

它有两个圆圈,⾥⾯的⼩圈圈是主题,⽽外⾯的⼤圈圈⾥放的是和这个主题有关的细节或特征。

基本形状是这样的:下⾯是国外⼀个幼⼉园孩⼦做的圆圈图练习。

左边是⼀个典型的联想型圆圈图;主题是海滩,可以联想到螃蟹、鱼、遮阳伞、海草、游泳⾐、海豚,等等。

⽽右边的图,反过来,从现象、特征(details)让孩⼦去推断相关的主题是什么?思维练习的开始就是这么简单!还可以⽤圆圈图帮孩⼦理解数学概念,虽然是⼀个简简单单的10以下数字,也可以让孩⼦展开很多思考和联想呢!2、⽓泡图,描述事物性质和特征Bubble Maps -Describing Qualities国外很多幼⼉园和⼩学都在⽤ Bubble Map 来帮助孩⼦学习知识、描述事物,因为这个真的⽐较简单和管⽤,最基本的⽓泡图是这样的:⽓泡图结构简单,逻辑⼀般只⾛⼀层,⽽且天然具有发散扩展的性质,所以特别适合⼩⼀点的孩⼦⽤它来多维度看问题,找到事物的多样特征,锻炼⼀下扩散性思维。

最大似然法精确重构不同状态混沌激光的相空间分布

最大似然法精确重构不同状态混沌激光的相空间分布

最大似然法精确重构不同状态混沌激光的相空间分布下载提示:该文档是本店铺精心编制而成的,希望大家下载后,能够帮助大家解决实际问题。

文档下载后可定制修改,请根据实际需要进行调整和使用,谢谢!本店铺为大家提供各种类型的实用资料,如教育随笔、日记赏析、句子摘抄、古诗大全、经典美文、话题作文、工作总结、词语解析、文案摘录、其他资料等等,想了解不同资料格式和写法,敬请关注!Download tips: This document is carefully compiled by this editor. I hope that after you download it, it can help you solve practical problems. The document can be customized and modified after downloading, please adjust and use it according to actual needs, thank you! In addition, this shop provides you with various types of practical materials, such as educational essays, diary appreciation, sentence excerpts, ancient poems, classic articles, topic composition, work summary, word parsing, copy excerpts, other materials and so on, want to know different data formats and writing methods, please pay attention!最大似然法精确重构不同状态混沌激光的相空间分布引言混沌现象在自然界中广泛存在,而混沌激光的相空间分布研究对于理解混沌系统的动力学行为至关重要。

地球椭球体(Ellipsoid)、大地基准面(Datum)及地图投影(Projection)三者的基本概念

地球椭球体(Ellipsoid)、大地基准面(Datum)及地图投影(Projection)三者的基本概念

高斯-克吕格投影与UTM投影高斯-克吕格(Gauss-Kruger)投影与UTM投影(Universal Transverse Mercator,通用横轴墨卡托投影)都是横轴墨卡托投影的变种,目前一些国外的软件或国外进口仪器的配套软件往往不支持高斯-克吕格投影,但支持UTM投影,因此常有把UTM投影当作高斯-克吕格投影的现象。

从投影几何方式看,高斯-克吕格投影是“等角横切圆柱投影”,投影后中央经线保持长度不变,即比例系数为1;UTM 投影是“等角横轴割圆柱投影”,圆柱割地球于南纬80度、北纬84度两条等高圈,投影后两条割线上没有变形,中央经线上长度比0.9996。

从计算结果看,两者主要差别在比例因子上,高斯-克吕格投影中央经线上的比例系数为1, UTM 投影为0.9996,高斯-克吕格投影与UTM投影可近似采用 X[UTM]=0.9996 * X[高斯],Y[UTM]=0.9996 * Y[高斯],进行坐标转换(注意:如坐标纵轴西移了500000米,转换时必须将Y值减去500000乘上比例因子后再加500000)。

从分带方式看,两者的分带起点不同,高斯-克吕格投影自0度子午线起每隔经差6度自西向东分带,第1带的中央经度为3°;UTM投影自西经180°起每隔经差6度自西向东分带,第1带的中央经度为-177°,因此高斯-克吕格投影的第1带是UTM 的第31带。

此外,两投影的东伪偏移都是500公里,高斯-克吕格投影北伪偏移为零,UTM北半球投影北伪偏移为零,南半球则为10000公里。

高斯-克吕格投影与UTM投影坐标系高斯- 克吕格投影与UTM投影是按分带方法各自进行投影,故各带坐标成独立系统。

以中央经线(L0)投影为纵轴X,赤道投影为横轴Y,两轴交点即为各带的坐标原点。

为了避免横坐标出现负值,高斯- 克吕格投影与UTM北半球投影中规定将坐标纵轴西移500公里当作起始轴,而UTM南半球投影除了将纵轴西移500公里外,横轴南移10000公里。

  1. 1、下载文档前请自行甄别文档内容的完整性,平台不提供额外的编辑、内容补充、找答案等附加服务。
  2. 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
  3. 3、如文档侵犯您的权益,请联系客服反馈,我们会尽快为您处理(人工客服工作时间:9:00-18:30)。

3
The Self-Dual Action
In [5,7] the Hamiltonian formulation of a complex self-dual action on a null hypersurface in Lorentzian space-time was studied. The 3+1 decomposition was inserted into the Lagrangian, and the constraints were derived with the usual Dirac’s procedure. In this section the results of [5] are briefly summarized and then compared with the corresponding constraints obtained by the multimomentum map. Since these constraints correspond to the secondary constraints of the Hamiltonian formalism [8], the discussion is focused on them. The complex self-dual part of the connection are the complex one-forms given by 1 i ˆc ˆ ˆ ˆc ˆ (+) ˆc ˆ ˆ bd ωa a = ωa a − ǫa . (10) ˆ ˆ ωa b d 2 2 Explicitly, one has
1 0 0 0 0 0 . 0 0 −1 0 −1 0
(7)
This implies that the hypersurfaces t =const. are null if and only if αˆ 1 + αˆ 2 αˆ 3 = 0. By a particular choice of coordinates, it is always possible to set αˆ 2 = αˆ 3 = 0.
(1)
(2)
The duals to these vectors are θ0 = (N + αi N i )dt + αi dxi and θk = νik (N i dt + dxi ) , where
i l l vk ˆ νi = δk ˆ ˆ ˆ ˆ ˆ ˆ
(3)
(4) (5) (6)

and
Multimomentum Maps in General Relativity
Байду номын сангаас
4
In many of the following equations the tetrad vectors appear in the combination e a c a c p ˜ aca (eˆ ec (9) ˆc ˆ= ˆ − ec ˆ ea ˆ) , 2 a ˆ where e = Nν with ν = det(νia ).
Multimomentum Maps in General Relativity of all the Einstein equations, one can introduce the null tetrad [5] eˆ 0 = and
i ek ˆ = vk ˆ ˆ + αk
3
1 N
∂ ∂ − Ni i ∂t ∂x Ni N ∂ αˆ ∂ − k . i ∂x N ∂t
Abstract The properties of multimomentum maps on null hypersurfaces, and their relation with the constraint analysis of General Relativity, are described. Unlike the case of spacelike hypersurfaces, some constraints which are second class in the Hamiltonian formalism turn out to contribute to the multimomentum map.
1
arXiv:gr-qc/9701020v1 10 Jan 1997
1
Introduction
In order to quantize gravity, a very long-time effort has been produced by physicists over the last fifty years. Since the perturbative approach fails to produce a renormalizable theory, it seemed more viable to proceed with the analysis of canonical gravity, which leads to a non-perturbative approach to quantum gravity. The canonical quantization of field theories follows Dirac’s prescription to translate the Poisson brackets among the canonical variables into commutators of the operators corresponding to these variables, and a special treatment is reserved to those systems and fields which are constrained. Within this framework Dirac, Bergmann, Arnowitt-Deser-Misner, Isham and, more recently, Ashtekar, pursued the aim of building a canonical formalism for General Relativity. The canonical approach has been successful, but it faces two important problems. First, to obtain a Hamiltonian formulation it is necessary to break
2
Null Tetrads
In this paper we are only concerned with the local treatment of the problem on null hypersurfaces. Thus, many problems arising from the possible null-cone singularities are left aside. To obtain a consistent 3+1 description
∗ †
Electronic address: cosmo@napoli.infn.it Electronic address: esposito@napoli.infn.it
1
Multimomentum Maps in General Relativity
2
manifest covariance. The other problem is that one has to deal with an infinite number of degrees of freedom when a field theory is considered. In recent work [1], the authors have studied a multisymplectic version of General Relativity. In this approach, field theories can be treated as an extension of the usual symplectic treatment of classical mechanics. Here, instead of working with an infinite number of degrees of freedom, as is usually done with the symplectic approach, the whole theory is constructed on a 1jet bundle, whose local coordinates are spacetime coordinates, the fields and their first derivatives. It has been shown, in particular, how the classical multisymplectic analysis of the constraints is equivalent to the constraint analysis given by Ashtekar from a canonical point of view [1]. Since the constraints were studied on a spacelike hypersurface, in this paper, to complete our previous investigations, null hypersurfaces are considered. In the Hamiltonian formulation of General Relativity, the constraint analysis on null hypersurfaces plays an important role since such surfaces provide a natural framework for the study of gravitational radiation in asymptotically flat space-times [2-7]. Moreover, in a null canonical formalism, the physical degrees of freedom and the observables of the theory may be picked out more easily [5,6]. Therefore it appears very interesting to extend the constraint analysis of [1] to null hypersurfaces and find out whether equivalent results exist. This may also provide further insight into the techniques for dealing with second-class constraints [8]. In section 2, null tetrads are defined according to [5]. In section 3, constraints are studied for a self-dual action. Concluding remarks are presented in section 4.
相关文档
最新文档