ABSTRACT Workgroup Middleware for Distributed Projects

SPM12Manual The FIL Methods Group (and honorary members)John AshburnerGareth BarnesChun-Chuan ChenJean DaunizeauGuillaume FlandinKarl FristonStefan KiebelJames KilnerVladimir LitvakRosalyn MoranWill PennyAdeel RaziKlaas StephanSungho TakPeter ZeidmanDarren GitelmanRik HensonChloe HuttonVolkmar GlaucheJérémie MattoutChristophe PhillipsFunctional Imaging LaboratoryWellcome Trust Centre for NeuroimagingInstitute of Neurology,UCL 12Queen Square,London WC1N3BG,UKJuly20,20162ContentsI Temporal processing171Slice Timing191.1Data (20)1.1.1Session (20)1.2Number of Slices (20)1.3TR (20)1.4TA (20)1.5Slice order (20)1.6Reference Slice (21)1.7Filename Prefix (21)II Spatial processing232Realign252.1Realign:Estimate (25)2.1.1Data (25)2.1.2Estimation Options (26)2.2Realign:Reslice (27)2.2.1Images (27)2.2.2Reslice Options (27)2.3Realign:Estimate&Reslice (28)2.3.1Data (28)2.3.2Estimation Options (28)2.3.3Reslice Options (29)3Realign&Unwarp313.1Data (34)3.1.1Session (34)3.2Estimation Options (34)3.2.1Quality (34)3.2.2Separation (34)3.2.3Smoothing(FWHM) (34)3.2.4Num Passes (34)3.2.5Interpolation (35)3.2.6Wrapping (35)3.2.7Weighting (35)3.3Unwarp Estimation Options (35)3.3.1Basis Functions (35)3.3.2Regularisation (35)3.3.3Reg.Factor (35)3.3.4Jacobian deformations (35)3.3.5First-order effects (36)3.3.6Second-order effects (36)3.3.8Re-estimate movement params (36)3.3.9Number of Iterations (36)3.3.10Taylor expansion point (36)3.4Unwarp Reslicing Options (36)3.4.1Resliced images(unwarp)? (36)3.4.2Interpolation (37)3.4.3Wrapping (37)3.4.4Masking (37)3.4.5Filename Prefix (37)4Coregister394.1Coregister:Estimate (39)4.1.1Reference Image (39)4.1.2Source Image (40)4.1.3Other Images (40)4.1.4Estimation Options (40)4.2Coregister:Reslice (40)4.2.1Image Defining Space (40)4.2.2Images to Reslice (40)4.2.3Reslice Options (40)4.3Coregister:Estimate&Reslice (41)4.3.1Reference Image (41)4.3.2Source Image (41)4.3.3Other Images (41)4.3.4Estimation Options (41)4.3.5Reslice Options (42)5Segment435.1Data (44)5.1.1Channel (44)5.2Tissues (45)5.2.1Tissue (45)5.3Warping&MRF (46)5.3.1MRF Parameter (46)5.3.2Clean Up (46)5.3.3Warping Regularisation (47)5.3.4Affine Regularisation (47)5.3.5Smoothness (47)5.3.6Sampling distance (48)5.3.7Deformation Fields (48)6Normalise496.1Normalise:Estimate (49)6.1.1Data (50)6.1.2Estimation Options (50)6.2Normalise:Write (51)6.2.1Data (51)6.2.2Writing Options (52)6.3Normalise:Estimate&Write (52)6.3.1Data (52)6.3.2Estimation Options (53)7Smooth577.1Images to Smooth (57)7.2FWHM (57)7.3Data Type (57)7.4Implicit masking (57)7.5Filename Prefix (57)III fMRI Statistics598fMRI model specification618.1Timing parameters (61)8.1.1Units for design (62)8.1.2Interscan interval (62)8.1.3Microtime resolution (62)8.1.4Microtime onset (62)8.2Data&Design (63)8.2.1Subject/Session (64)8.3Factorial design (65)8.3.1Factor (66)8.4Basis Functions (66)8.4.1Canonical HRF (66)8.4.2Other basis sets (66)8.5Model Interactions(Volterra) (67)8.6Directory (67)8.7Global normalisation (67)8.8Explicit mask (67)8.9Serial correlations (68)8.10Reviewing your design (68)9fMRI model estimation719.1Select SPM.mat (71)9.2Method (71)9.2.1Classical (71)9.2.2Bayesian1st-level (72)9.2.3Bayesian2nd-level (75)9.3Outputfiles (76)9.3.1Classical1st-level (76)9.3.2Bayesian1st-level (76)9.4Model comparison (76)10Factorial design specification7910.1Directory (80)10.2Design (80)10.2.1One-sample t-test (80)10.2.2Two-sample t-test (80)10.2.3Paired t-test (81)10.2.4Multiple regression (81)10.2.5One-way ANOVA (82)10.2.6One-way ANOVA-within subject (83)10.2.7Full factorial (84)10.2.8Flexible factorial (86)10.2.9Partitioned model (88)10.3Covariates (88)10.3.1Covariate (88)10.4Multiple covariates (89)10.5Masking (90)10.5.1Threshold masking (90)10.5.2Implicit Mask (90)10.5.3Explicit Mask (91)10.6Global calculation (91)10.6.1Omit (91)10.6.2User (91)10.6.3Mean (91)10.7Global normalisation (91)10.7.1Overall grand mean scaling (91)10.7.2Normalisation (92)IV EEG/MEG9311SPM for MEG/EEG overview9511.1Welcome to SPM for M/EEG (95)11.2Changes from SPM8to SPM12 (96)12EEG/MEG preprocessing–Reference9712.1Conversion of data (97)12.2Converting arbitrary data (99)12.3The M/EEG SPM format (99)12.4Preparing the data after conversion and specifying batch inputs (100)12.4.1Prepare(batch) (104)12.5Integration of SPM and Fieldtrip (104)12.6Loading data into workspace (104)12.7The meeg object (104)12.7.1Constructor meeg (105)12.7.2Array-like interface (105)12.7.3display (105)12.7.4Number methods (105)12.7.5Reading and manipulation of information (105)12.7.6Reading of information (108)12.7.7Manipulations of the data on disk (109)12.7.8Struct-like interface (110)12.8SPM functions (110)12.8.1Epoching the data:spm_eeg_epochs (110)12.8.2Filtering the data:spm_eeg_filter (111)12.8.3Baseline correction:spm_eeg_bc (111)12.8.4Artefact detection and rejection:spm_eeg_artefact (111)12.8.5Downsampling:spm_eeg_downsample (112)12.8.6Rereferencing:spm_eeg_montage (112)12.8.7Grand mean:spm_eeg_grandmean (112)12.8.8Merge:spm_eeg_merge (112)12.8.9Multimodal fusion:spm_eeg_fuse (113)12.8.10Cropping:spm_eeg_crop (113)12.8.11Combine planar:spm_eeg_combineplanar (113)12.8.12Data reduction:spm_eeg_reduce (113)12.8.13Time-frequency decomposition:spm_eeg_tf (113)12.8.14Rescaling and baseline correction of time-frequency:spm_eeg_tf_rescale.11412.8.15Averaging over time or frequency:spm_eeg_avgtime,spm_eeg_avgfreq..11412.8.16Averaging:spm_eeg_average (114)12.8.17Contrast over epochs:spm_eeg_contrast (114)12.8.18Copy:spm_eeg_copy (115)12.8.19Remove bad trials:spm_eeg_remove_bad_trials (115)12.9.1Data visualization (116)12.9.2Source reconstructions visualization (116)12.9.3Script generation (117)13Analysis in sensor space11913.0.4Output (119)13.0.5Smoothing (120)143D source reconstruction:Imaging approach12114.1Introduction (121)14.2Getting started (122)14.3Source space modeling (122)14.4Coregistration (123)14.5Forward computation(forward) (124)14.6Inverse reconstruction (125)14.7Summarizing the results of inverse reconstruction as an image (126)14.8Rendering interface (127)14.9Group inversion (127)14.10Batching source reconstruction (127)14.11Appendix:Data structure (127)15Localization of Equivalent Current Dipoles12915.1Introduction (129)15.2Procedure in SPM12 (130)15.2.1Head and forward model (130)15.2.2VB-ECD reconstruction (130)15.2.3Result display (131)16Dynamic Causal Modelling for M/EEG13316.1Introduction (133)16.2Overview (134)16.3Calling DCM for ERP/ERF (134)16.4load,save,select model type (135)16.5Data and design (135)16.6Electromagnetic model (136)16.7Neuronal model (136)16.8Estimation (137)16.9Results (137)16.10Cross-spectral densities (138)16.10.1Model specification (138)16.10.2The Lead-Field (138)16.10.3Connections (138)16.10.4Cross Spectral Densities (138)16.10.5Output and Results (139)16.11Induced responses (139)16.11.1Data (139)16.11.2Electromagnetic model (139)16.11.3Neuronal model (139)16.11.4Wavelet transform (139)16.11.5Results (139)16.12Phase-coupled responses (140)16.12.1Data (140)16.12.2Electromagnetic model (140)16.12.3Neuronal model (140)16.12.4Hilbert transform (140)V Utilities143 17Display Image14517.1Image to Display (146)18Check Registration14918.1Images to Display (149)19Rendering15119.1Surface Extraction (151)19.1.1Input Images (151)19.1.2Surfaces (151)19.2Surface Rendering (152)19.2.1Objects (152)19.2.2Lights (153)20Image Calculator15520.1Input Images (155)20.2Output Filename (155)20.3Output Directory (155)20.4Expression (156)20.5Additional Variables (156)20.5.1Variable (156)20.6Options (156)20.6.1Data Matrix (156)20.6.2Masking (156)20.6.3Interpolation (156)20.6.4Data Type (157)21Import15921.1DICOM Import (159)21.1.1DICOMfiles (159)21.1.2Output directory (160)21.1.3Directory structure (160)21.1.4Protocol namefilter (160)21.1.5Conversion options (160)21.2MINC Import (160)21.2.1MINCfiles (160)21.2.2Options (160)21.3ECAT Import (161)21.3.1ECATfiles (161)21.3.2Options (161)21.4PAR/REC Import (161)21.4.1PARfiles (161)21.4.2Options (161)22De-face Images16322.1Images to de-face (163)23Deformations16523.1Composition (165)23.1.1Dartelflow (165)23.1.2Deformation Field (166)23.1.3Identity(Reference Image) (166)23.1.4Identity(Bounding Box and Voxel Size) (166)23.1.5Imported_sn.mat (167)23.1.6Inverse (167)23.2.1Save Deformation (167)23.2.2Pullback (168)23.2.3Pushforward (168)23.2.4Surface (170)23.2.5Save Jacobian Determinants (170)VI Tools17124FieldMap Toolbox17324.1Introduction (173)24.2Presubtracted Phase and Magnitude Data (173)24.2.1Data (173)24.3Real and Imaginary Data (175)24.3.1Data (176)24.4Phase and Magnitude Data (176)24.4.1Data (176)24.5Precalculated FieldMap(in Hz) (176)24.5.1Data (176)24.6Apply VDM (177)24.6.1Data (177)24.6.2Reslice Options (177)24.7Creating Field Maps Using the FieldMap GUI (178)24.7.1Createfield map in Hz (178)24.7.2Create voxel displacement map(VDM)and unwarp EPI (181)24.8Using the FieldMap in Batch scripts (182)24.9Using the VDMfile with Unwarp (183)24.10Appendices (183)24.10.1Processing Hzfield maps (183)24.10.2Converting Hzfield map to VDM (184)24.10.3Matchingfield map data to EPI data (184)25Dartel Tools18525.1Initial Import (186)25.1.1Parameter Files (186)25.1.2Output Directory (187)25.1.3Bounding box (187)25.1.4Voxel size (187)25.1.5Image option (187)25.1.6Grey Matter (187)25.1.7White Matter (187)25.1.8CSF (187)25.2Run Dartel(create Templates) (187)25.2.1Images (187)25.2.2Settings (187)25.3Run Dartel(existing Templates) (189)25.3.1Images (189)25.3.2Settings (189)25.4Normalise to MNI Space (190)25.4.1Dartel Template (190)25.4.2Select according to (190)25.4.3Voxel sizes (191)25.4.4Bounding box (191)25.4.5Preserve (191)25.4.6Gaussian FWHM (191)25.5Create Warped (191)25.5.2Images (192)25.5.3Modulation (192)25.5.4Time Steps (192)25.5.5Interpolation (192)25.6Jacobian determinants (192)25.6.1Flowfields (192)25.6.2Time Steps (192)25.7Create Inverse Warped (193)25.7.1Flowfields (193)25.7.2Images (193)25.7.3Time Steps (193)25.7.4Interpolation (193)25.8Population to ICBM Registration (193)25.8.1Dartel Template (193)25.9Kernel Utilities (193)25.9.1Kernel from Images (194)25.9.2Kernel from Flows (194)26Shoot Tools19526.1Run Shooting(create Templates) (195)26.1.1Images (196)26.2Run Shoot(existing Templates) (196)26.2.1Images (196)26.2.2Templates (196)26.3Kernel Utilities (196)26.3.1Kernel from velocities (196)26.3.2Generate Scalar Momenta (197)26.3.3Kernel from Images (197)27Longitudinal Registration19927.1Pairwise Longitudinal Registration (199)27.1.1Time1Volumes (200)27.1.2Time2Volumes (200)27.1.3Time Difference (200)27.1.4Noise Estimate (200)27.1.5Warping Regularisation (200)27.1.6Bias Regularisation (200)27.1.7Save Mid-point average (201)27.1.8Save Jacobian Rate (201)27.1.9Save Divergence Rate (201)27.1.10Deformation Fields (201)27.2Serial Longitudinal Registration (201)27.2.1Volumes (201)27.2.2Times (202)27.2.3Noise Estimate (202)27.2.4Warping Regularisation (202)27.2.5Bias Regularisation (202)27.2.6Save Mid-point average (203)27.2.7Save Jacobians (203)27.2.8Save Divergence (203)28Old Normalise20528.1Old Normalise:Estimate (206)28.1.1Data (206)28.1.2Estimation Options (206)28.2Old Normalise:Write (207)28.2.1Data (207)28.2.2Writing Options (208)28.3Old Normalise:Estimate&Write (208)28.3.1Data (208)28.3.2Estimation Options (209)28.3.3Writing Options (210)29Old Segment21129.1Data (212)29.2Output Files (212)29.2.1Grey Matter (215)29.2.2White Matter (215)29.2.3Cerebro-Spinal Fluid (215)29.2.4Bias Corrected (215)29.2.5Clean up any partitions (215)29.3Custom (215)29.3.1Tissue probability maps (215)29.3.2Gaussians per class (216)29.3.3Affine Regularisation (216)29.3.4Warping Regularisation (216)29.3.5Warp Frequency Cutoff (217)29.3.6Bias regularisation (217)29.3.7Bias FWHM (217)29.3.8Sampling distance (217)29.3.9Masking image (217)VII Data sets and examples21930Auditory fMRI data22130.1Preamble(dummy scans) (221)30.2Spatial pre-processing (223)30.2.1Realignment (223)30.2.2Coregistration (223)30.2.3Segmentation (223)30.2.4Normalise (227)30.2.5Smoothing (227)30.3Model specification,review and estimation (231)30.3.1Estimate (231)30.4Inference (231)30.4.1Contrast manager (235)30.4.2Masking (235)30.4.3Thresholds (235)30.4.4Files (236)30.4.5Maximum Intensity Projections (236)30.4.6Design matrix (238)30.4.7Statistical tables (238)30.4.8Plotting responses at a voxel (239)31Face fMRI data24331.1Spatial pre-processing (243)31.1.1Display (243)31.1.2Realignment (243)31.1.3Slice timing correction (248)31.1.4Coregistration (248)31.1.5Segmentation (248)31.1.6Normalise (251)31.1.7Smoothing (251)31.2Modelling categorical responses (253)31.2.1Estimate (255)31.2.2Inference for categorical design (255)31.2.3Statistical tables (255)31.2.4F-contrasts (257)31.2.5F-contrasts for testing effects of movement (261)31.3Modelling parametric responses (261)31.3.1Estimate (263)31.3.2Plotting parametric responses (263)31.4Bayesian analysis (266)31.4.1Specification (266)31.4.2Estimation (267)31.4.3Inference (268)32Face group fMRI data27132.1Introduction (271)32.2Data (271)32.3Canonical HRF (272)32.4Informed basis set (274)32.4.1Nonsphericity (276)32.4.2Informed Results (276)32.4.3T-and F-contrasts (280)32.5FIR basis set (282)32.5.1Nonsphericity again (283)32.5.2FIR Results (286)33Mixed Effects Analysis29133.1Introduction (291)34Verbal Fluency PET data29334.1Introduction (293)34.2Single subject (293)34.3Multiple subjects (294)34.3.1Subject and Condition design (296)34.3.2Subject and Time design (296)34.3.3Subject by Condition design (298)34.3.4Contrast manager (300)34.3.5Masking and thresholds (302)34.3.6MIPs and results tables (303)34.3.7Small volume correction (305)34.3.8Extracting data from regions (305)34.3.9Inclusive Masking (307)35Dynamic Causal Modeling for fMRI31135.1Theoretical background (311)35.2Bayesian model selection (314)35.3Practical example (315)35.3.1Defining the GLM (316)35.3.2Extracting time series (317)35.3.3Specifying and estimating the DCM (318)35.3.4Comparing models (321)36Psychophysiological Interactions(PPI)32536.1Theoretical background (325)36.2Psycho-Physiologic Interaction Analysis:Summary of Steps (327)36.3Practical example (327)36.3.1GLM analysis-Design setup and estimation (328)36.3.2GLM analysis-Results (332)36.4GLM analysis-Extracting VOIs (334)36.5PPI analysis-Create PPI variable (334)36.5.1PPI GLM analysis-Design setup and estimation (336)36.5.2PPI analysis-Results (337)36.5.3PPI analysis-Plotting (338)37Bayesian Model Inference34137.1Background (341)37.2Data (341)37.3Analysis (342)37.3.1Single Family (342)37.3.2Bayesian Model Averaging (345)37.3.3Family level inference (345)37.3.4Summary Statistics and Group Analyses (345)37.4BMS.matfile (345)37.4.1Model level results (349)37.4.2Family level results (349)37.4.3Bayesian model averaging(BMA) (349)37.5model_space.matfile (350)38Dynamic Causal Modelling for resting state fMRI35138.1Theoretical background (351)38.2Practical example (353)38.2.1Defining the GLM (353)38.2.2Extracting time series (355)38.2.3Specifying and estimating the DCM (355)39MEG source localisation36139.1Overview (361)39.2Simulation (361)39.3Imaging solutions for evoked or induced responses (363)39.3.1IID(minimum norm) (363)39.3.2Smooth priors(COH) (364)39.3.3The Multiple sparse priors algorithm (364)39.3.4Making summary images (366)39.3.5Other MSP options (366)39.4Dipolefitting to the average (367)39.4.1Load/preview the data (367)40EEG Mismatch negativity data37140.1Preprocessing (371)40.1.1Simple conversion and reviewing (371)40.1.2Preparing batch inputs (372)40.1.3Preprocessing step by step (373)40.1.4Automatisation of preprocessing (376)40.2Sensor space analysis (378)40.2.1Batching statistics (379)40.3Source reconstruction (379)40.3.1Mesh (379)40.3.2Coregister (381)40.3.3Forward model (381)40.3.4Invert (381)40.3.5Batching source reconstruction (382)40.4Dynamic Causal Modeling (386)41Advanced topics in M/EEG artefact removal39141.1Artefact marking (391)41.2Reviewing marked artefacts (392)41.3Trial rejection based on marked artefacts (392)41.4Explicit artefact exclusion in robust averaging (393)41.5Topography-based artefact correction (393)41.6Fieldtrip visual artefact rejection (398)42Multimodal,Multisubject data fusion40142.1Overview (401)42.2Getting Started (402)42.3Preprocessing M/EEG data (402)42.3.1Convert(and epoch) (402)42.3.2Prepare (404)42.3.3Downsample (404)42.3.4Baseline Correction (405)42.3.5Deleting intermediate steps(optional) (405)42.3.6Merging(concatenating runs) (406)42.3.7Prepare(a montage for re-referencing the EEG) (406)42.3.8Montage (407)42.4Evoked analysis (407)42.4.1Crop (407)42.4.2Artefact detection (408)42.4.3Combine Planar Gradiometers (408)42.4.4Trial averaging (408)42.4.5Contrasting conditions (408)42.4.6Time-Sensor images (410)42.5Scalp-Time Statistics across trials within one subject (413)42.5.1Model Specification (413)42.5.2Model Estimation (413)42.5.3Setting up contrasts (413)42.6Time-Frequency Analysis(Evoked and Induced power) (414)42.6.1Wavelet estimation (416)42.6.2Crop (416)42.6.3Average (417)42.6.4Baseline rescaling (417)42.6.5Contrasting conditions (417)42.6.6Creating2D time-frequency images (417)42.6.7Model Specification,Estimation and Contrasts (420)42.7fMRI Preprocessing and Statistics (422)42.7.2Normalisation/Segmentation of T1images (422)42.7.3Coregistration of mean EPI(fMRI)to T1(sMRI) (422)42.7.4Application of Normalisation parameters to EPI data (422)42.7.5Smoothing (423)42.7.6Creating a1st-level(fMRI)GLM (423)42.7.7Model Estimation (423)42.7.8Setting up contrasts (423)42.7.9Group Statistics on fMRI data (425)42.8Source Reconstruction (425)42.8.1Create Head Model (427)42.8.2Model Inversion (428)42.8.3Time-frequency contrasts (428)42.8.4Group Statistics on Source Reconstructions (431)42.9Group Source Reconstruction (433)42.9.1Group Statistics on Source Reconstructions (433)42.10Group MEEG Source Reconstruction with fMRI priors (433)42.10.1Group Statistics on Source Reconstructions (435)42.11References (435)42.12Acknowledgements (437)43DCM for Induced Responses43943.1Data (439)43.2Getting Started (439)43.3Setting up DCM (439)43.3.1load,save,select model type (440)43.3.2Data and design (440)43.3.3Electromagnetic model (442)43.4Neuronal model (444)43.5Estimation (445)43.6Results (446)43.6.1Frequency modes (446)43.6.2Time modes (446)43.6.3Time-Frequency (446)43.6.4Coupling(A-Hz) (446)43.6.5Coupling(B-Hz) (446)43.6.6Coupling(A-modes) (446)43.6.7Coupling(B-Hz) (446)43.6.8Input(C-Hz) (447)43.6.9Input(u-ms) (447)43.6.10Dipoles (447)43.6.11Save as img (447)43.7Model comparison (447)44DCM for Phase Coupling44944.1Data (449)44.2Getting Started (449)44.3Data and design (449)44.4Electromagnetic model (450)44.5Neuronal model (450)44.6Results (451)45DCM for Cross Spectral Densities:Anaesthesia Depth in Rodent Data45545.1Overview (455)45.2Main Results (456)45.3Using the Graphical User Interface to Obtain those Results (456)45.3.1The data (456)45.3.2Dynamic Causal Modelling of Cross Spectral Densities (456)45.3.3Comparing models using Bayesian Model Selection (460)46DCM for fNIRS46346.1Example:Motor Execution and Imagery Data (464)46.2SPM Analysis (464)46.3Specifying and Estimating the DCM (464)47Using Dartel47147.1Using Dartel for VBM (471)47.1.1Using Spatial→Segment (471)47.1.2Using Dartel Tools→Run Dartel(create Template) (472)47.1.3Using Dartel Tools→Normalise to MNI Space (472)47.2Spatially normalising functional data to MNI space (474)47.2.1An alternative approach for using Dartel to spatially normalise to MNI Space47747.3Warping Images to Existing Templates (479)47.4Warping one individual to match another (479)VIII Batch Interface48348Batch tutorial48548.1Single subject (485)48.1.1Study specific input data (486)48.1.2Necessary processing steps (486)48.1.3Add modules to the batch (486)48.1.4Configure subject-independent data (486)48.1.5Dataflow (488)48.1.6Entering subject-specific data (491)48.2Advanced features (491)48.2.1Multiple sessions (491)48.2.2Processing multiple subjects in GUI (493)48.2.3Command line interface (493)48.2.4Modifying a saved job (495)49Developer’s guide49749.1SPM and Matlabbatch code organisation (497)49.1.1Code organisation (497)49.1.2Interfaces between SPM and Matlabbatch (497)49.2Configuration Code Details (497)49.2.1Virtual Outputs (498)49.2.2SPM Startup (498)49.2.3Defaults Settings (498)49.3Utilities (499)49.3.1Batch Utilities (499)49.3.2MATLAB Code Generation (499)49.3.3Configuration Management (499)IX Bibliography501。

ABAQUS/CAE 常问界面操作(转自SimWe仿真论坛) 2009-08-01 21:40 | (分类: 默认分类)前处理：1 如何显示梁截面（如何显示三维梁模型）a）无论是运算还是默认显示，ABA中的梁都是一条线，很多人想看梁截面（一般一个星期有人问一次）。

显示梁截面：view->assembly display option->render beam profiles，自己调节系数/viewthread.php?tid=835478&page=1#pid1531086b）后处理到底能不能显示梁截面？在deformed shape和undeformed shape都能用上面的方法显示梁截面，在应力云图（contour）不能显示。

c）也经常有人问起如何显示壳单元的厚度/thread-865887-1-1.html2 怎么在局部坐标系下建立参考点在前处理中，已经建立了局部坐标系，如何在局部坐标系中建立参考点？这个有点麻烦，看看konadoul图文并茂的示例吧。

/viewthread.php?tid=863389&highlight=%D7%F8%B1%EA%CF%B 53 Documentaion（help文件）不能搜索首先保证你准确的安装了Documentaion（先安装Documentaion再安装程序），其次有问题你可以重新安装一次Documentaion。

如果你和我一样比较懒不想安装，看看下面的方法是否管用吧。

1）控制面板---服务找到texis 双击查看是不是automatic，如果不是就设置为automatic2）你可以用这个：http://name:2080/v6.8/ 注：name是你的计算机名；6.8是版本号，比如你用6.6的就改为6.6.（我的在自从不能搜索之后我就一直这么用的）/viewthread.php?tid=861085&extra=page%3D12%26amp%3Bfilter %3Dtype%26amp%3Btypeid%3D68/viewthread.php?tid=6988794 建立几何模型草绘sketch的时候，发现画布尺寸太小了1）这个在create part的时候就有approximate size，你可以定义合适的（比你的定性尺寸大一倍）；如果你已经在sketch了，可以在edit菜单－－sketch option －－grid更改2）这里如果你选择constriant标签，还能更改尺寸精度5 想输出几何模型part步，file，outport－－part想导入几何模型？part步，file，import－－part6 如何定义局部坐标系Tool－Create Datum－CSYS－－建立坐标系方式－－选择直角坐标系or柱坐标系or 球坐标7 如何在局部坐标系定义载荷laod－－Edit load－－CSYS－Edit（在BC中同理）选用你定义的局部坐标系8 如何定义随变载荷amplitude 这个不多说了，强烈建议看《常见问题2.0》小康大侠图示空间变载：/thread-867236-1-5.html （强烈推荐照此演示操作一回）/viewthread.php?tid=861727&highlight=%CB%E6%CA%B1%BC %E4%B1%E4%BB%AF%B5%C4%D4%D8%BA%C9設定於空間中變化的負載：A; v% |!/viewthrea ... p;page=1#pid1556636加载梯形载荷：/viewthread.php?tid=870350&extra=page%3D1%26amp%3Bfilter%3Dty pe%26amp%3Btypeid%3D689 怎么知道模型单元数目（一共有多少个单元）在mesh步，mesh verify 可以查到单元类型，数目以及单元质量一目了然Query---element 也可以查询的。

人工智能是一门新兴的具有挑战力的学科。

自人工智能诞生以来,发展迅速,产生了许多分支。

诸如强化学习、模拟环境、智能硬件、机器学习等。

但是,在当前人工智能技术迅猛发展,为人们的生活带来许多便利。

下面是搜索整理的人工智能英文参考文献的分享，供大家借鉴参考。

人工智能英文参考文献一：[1]Lars Egevad,Peter Str?m,Kimmo Kartasalo,Henrik Olsson,Hemamali Samaratunga,Brett Delahunt,Martin Eklund. The utility of artificial intelligence in the assessment of prostate pathology[J]. Histopathology,2020,76(6).[2]Rudy van Belkom. The Impact of Artificial Intelligence on the Activities ofa Futurist[J]. World Futures Review,2020,12(2).[3]Reza Hafezi. How Artificial Intelligence Can Improve Understanding in Challenging Chaotic Environments[J]. World Futures Review,2020,12(2).[4]Alejandro Díaz-Domínguez. How Futures Studies and Foresight Could Address Ethical Dilemmas of Machine Learning and Artificial Intelligence[J]. World Futures Review,2020,12(2).[5]Russell T. Warne,Jared Z. Burton. Beliefs About Human Intelligence in a Sample of Teachers and Nonteachers[J]. Journal for the Education of the Gifted,2020,43(2).[6]Russell Belk,Mariam Humayun,Ahir Gopaldas. Artificial Life[J]. Journal of Macromarketing,2020,40(2).[7]Walter Kehl,Mike Jackson,Alessandro Fergnani. Natural Language Processing and Futures Studies[J]. World Futures Review,2020,12(2).[8]Anne Boysen. Mine the Gap: Augmenting Foresight Methodologies with Data Analytics[J]. World Futures Review,2020,12(2).[9]Marco Bevolo,Filiberto Amati. The Potential Role of AI in Anticipating Futures from a Design Process Perspective: From the Reflexive Description of “Design” to a Discussion of Influences by the Inclusion of AI in the Futures Research Process[J]. World Futures Review,2020,12(2).[10]Lan Xu,Paul Tu,Qian Tang,Dan Seli?teanu. Contract Design for Cloud Logistics (CL) Based on Blockchain Technology (BT)[J]. Complexity,2020,2020.[11]L. Grant,X. Xue,Z. Vajihi,A. Azuelos,S. Rosenthal,D. Hopkins,R. Aroutiunian,B. Unger,A. Guttman,M. Afilalo. LO32: Artificial intelligence to predict disposition to improve flow in the emergency department[J]. CJEM,2020,22(S1).[12]A. Kirubarajan,A. Taher,S. Khan,S. Masood. P071: Artificial intelligence in emergency medicine: A scoping review[J]. CJEM,2020,22(S1).[13]L. Grant,P. Joo,B. Eng,A. Carrington,M. Nemnom,V. Thiruganasambandamoorthy. LO22: Risk-stratification of emergency department syncope by artificial intelligence using machine learning: human, statistics or machine[J]. CJEM,2020,22(S1).[14]Riva Giuseppe,Riva Eleonora. OS for Ind Robots: Manufacturing Robots Get Smarter Thanks to Artificial Intelligence.[J]. Cyberpsychology, behavior and social networking,2020,23(5).[15]Markus M. Obmann,Aurelio Cosentino,Joshy Cyriac,Verena Hofmann,Bram Stieltjes,Daniel T. Boll,Benjamin M. Yeh,Matthias R. Benz. Quantitative enhancement thresholds and machine learning algorithms for the evaluation of renal lesions using single-phase split-filter dual-energy CT[J]. Abdominal Radiology,2020,45(1).[16]Haytham H. Elmousalami,Mahmoud Elaskary. Drilling stuck pipe classification and mitigation in the Gulf of Suez oil fields using artificial intelligence[J]. Journal of Petroleum Exploration and Production Technology,2020,10(10).[17]Rüdiger Schulz-Wendtland,Karin Bock. Bildgebung in der Mammadiagnostik –Ein Ausblick <trans-title xml:lang="en">Imaging in breast diagnostics—an outlook [J]. Der Gyn?kologe,2020,53(6).</trans-title>[18]Nowakowski Piotr,Szwarc Krzysztof,Boryczka Urszula. Combining an artificial intelligence algorithm and a novel vehicle for sustainable e-waste collection[J]. Science of the Total Environment,2020,730.[19]Wang Huaizhi,Liu Yangyang,Zhou Bin,Li Canbing,Cao Guangzhong,Voropai Nikolai,Barakhtenko Evgeny. Taxonomy research of artificial intelligence for deterministic solar power forecasting[J]. Energy Conversion and Management,2020,214.[20]Kagemoto Hiroshi. Forecasting a water-surface wave train with artificial intelligence- A case study[J]. Ocean Engineering,2020,207.[21]Tomonori Aoki,Atsuo Yamada,Kazuharu Aoyama,Hiroaki Saito,Gota Fujisawa,Nariaki Odawara,Ryo Kondo,Akiyoshi Tsuboi,Rei Ishibashi,Ayako Nakada,Ryota Niikura,Mitsuhiro Fujishiro,Shiro Oka,Soichiro Ishihara,Tomoki Matsuda,Masato Nakahori,Shinji Tanaka,Kazuhiko Koike,Tomohiro Tada. Clinical usefulness of a deep learning‐based system as the first screening on small‐bowel capsule endoscopy reading[J]. Digestive Endoscopy,2020,32(4).[22]Masashi Fujii,Hajime Isomoto. Next generation of endoscopy: Harmony with artificial intelligence and robotic‐assisted devices[J]. Digestive Endoscopy,2020,32(4).[23]Roberto Verganti,Luca Vendraminelli,Marco Iansiti. Innovation and Design in the Age of Artificial Intelligence[J]. Journal of Product Innovation Management,2020,37(3).[24]Yuval Elbaz,David Furman,Maytal Caspary Toroker. Modeling Diffusion in Functional Materials: From Density Functional Theory to Artificial Intelligence[J]. Advanced Functional Materials,2020,30(18).[25]Dinesh Visva Gunasekeran,Tien Yin Wong. Artificial Intelligence in Ophthalmology in 2020: A Technology on the Cusp for Translation and Implementation[J]. Asia-Pacific Journal of Ophthalmology,2020,9(2).[26]Fu-Neng Jiang,Li-Jun Dai,Yong-Ding Wu,Sheng-Bang Yang,Yu-Xiang Liang,Xin Zhang,Cui-Yun Zou,Ren-Qiang He,Xiao-Ming Xu,Wei-De Zhong. The study of multiple diagnosis models of human prostate cancer based on Taylor database by artificial neural networks[J]. Journal of the Chinese Medical Association,2020,83(5).[27]Matheus Calil Faleiros,Marcello Henrique Nogueira-Barbosa,Vitor Faeda Dalto,JoséRaniery Ferreira Júnior,Ariane Priscilla Magalh?es Tenório,Rodrigo Luppino-Assad,Paulo Louzada-Junior,Rangaraj Mandayam Rangayyan,Paulo Mazzoncini de Azevedo-Marques. Machine learning techniques for computer-aided classification of active inflammatory sacroiliitis in magnetic resonance imaging[J]. Advances in Rheumatology,2020,60(1078).[28]Balamurugan Balakreshnan,Grant Richards,Gaurav Nanda,Huachao Mao,Ragu Athinarayanan,Joseph Zaccaria. PPE Compliance Detection using Artificial Intelligence in Learning Factories[J]. Procedia Manufacturing,2020,45.[29]M. Stévenin,V. Avisse,N. Ducarme,A. de Broca. Qui est responsable si un robot autonome vient à entra?ner un dommage ?[J]. Ethique et Santé,2020.[30]Fatemeh Barzegari Banadkooki,Mohammad Ehteram,Fatemeh Panahi,Saad Sh. Sammen,Faridah Binti Othman,Ahmed EL-Shafie. Estimation of Total Dissolved Solids (TDS) using New Hybrid Machine Learning Models[J]. Journal of Hydrology,2020.[31]Adam J. Schwartz,Henry D. Clarke,Mark J. Spangehl,Joshua S. Bingham,DavidA. Etzioni,Matthew R. Neville. Can a Convolutional Neural Network Classify Knee Osteoarthritis on Plain Radiographs as Accurately as Fellowship-Trained Knee Arthroplasty Surgeons?[J]. The Journal of Arthroplasty,2020.[32]Ivana Nizetic Kosovic,Toni Mastelic,Damir Ivankovic. Using Artificial Intelligence on environmental data from Internet of Things for estimating solar radiation: Comprehensive analysis[J]. Journal of Cleaner Production,2020.[33]Lauren Fried,Andrea Tan,Shirin Bajaj,Tracey N. Liebman,David Polsky,Jennifer A. Stein. Technological advances for the detection of melanoma: Part I. Advances in diagnostic techniques[J]. Journal of the American Academy of Dermatology,2020.[34]Mohammed Amoon,Torki Altameem,Ayman Altameem. Internet of things Sensor Assisted Security and Quality Analysis for Health Care Data Sets Using Artificial Intelligent Based Heuristic Health Management System[J]. Measurement,2020.[35]E. Lotan,C. Tschider,D.K. Sodickson,A. Caplan,M. Bruno,B. Zhang,Yvonne W. Lui. Medical Imaging and Privacy in the Era of Artificial Intelligence: Myth, Fallacy, and the Future[J]. Journal of the American College of Radiology,2020.[36]Fabien Lareyre,Cédric Adam,Marion Carrier,Juliette Raffort. Artificial Intelligence in Vascular Surgery: moving from Big Data to Smart Data[J]. Annals of Vascular Surgery,2020.[37]Ilesanmi Daniyan,Khumbulani Mpofu,Moses Oyesola,Boitumelo Ramatsetse,Adefemi Adeodu. Artificial intelligence for predictive maintenance in the railcar learning factories[J]. Procedia Manufacturing,2020,45.[38]Janet L. McCauley,Anthony E. Swartz. Reframing Telehealth[J]. Obstetrics and Gynecology Clinics of North America,2020.[39]Jean-Emmanuel Bibault,Lei Xing. Screening for chronic obstructive pulmonary disease with artificial intelligence[J]. The Lancet Digital Health,2020,2(5).[40]Andrea Laghi. Cautions about radiologic diagnosis of COVID-19 infection driven by artificial intelligence[J]. The Lancet Digital Health,2020,2(5).人工智能英文参考文献二：[41]K. Orhan,I. S. Bayrakdar,M. Ezhov,A. Kravtsov,T. ?zyürek. Evaluation of artificial intelligence for detecting periapical pathosis on cone‐beam computed tomography scans[J]. International Endodontic Journal,2020,53(5).[42]Avila A M,Mezi? I. Data-driven analysis and forecasting of highway traffic dynamics.[J]. Nature communications,2020,11(1).[43]Neri Emanuele,Miele Vittorio,Coppola Francesca,Grassi Roberto. Use of CT andartificial intelligence in suspected or COVID-19 positive patients: statement of the Italian Society of Medical and Interventional Radiology.[J]. La Radiologia medica,2020.[44]Tau Noam,Stundzia Audrius,Yasufuku Kazuhiro,Hussey Douglas,Metser Ur. Convolutional Neural Networks in Predicting Nodal and Distant Metastatic Potential of Newly Diagnosed Non-Small Cell Lung Cancer on FDG PET Images.[J]. AJR. American journal of roentgenology,2020.[45]Coppola Francesca,Faggioni Lorenzo,Regge Daniele,Giovagnoni Andrea,Golfieri Rita,Bibbolino Corrado,Miele Vittorio,Neri Emanuele,Grassi Roberto. Artificial intelligence: radiologists' expectations and opinions gleaned from a nationwide online survey.[J]. La Radiologia medica,2020.[46]?. ? ? ? ? [J]. ,2020,25(4).[47]Savage Rock H,van Assen Marly,Martin Simon S,Sahbaee Pooyan,Griffith Lewis P,Giovagnoli Dante,Sperl Jonathan I,Hopfgartner Christian,K?rgel Rainer,Schoepf U Joseph. Utilizing Artificial Intelligence to Determine Bone Mineral Density Via Chest Computed Tomography.[J]. Journal of thoracic imaging,2020,35 Suppl 1.[48]Brzezicki Maksymilian A,Bridger Nicholas E,Kobeti? Matthew D,Ostrowski Maciej,Grabowski Waldemar,Gill Simran S,Neumann Sandra. Artificial intelligence outperforms human students in conducting neurosurgical audits.[J]. Clinical neurology and neurosurgery,2020,192.[49]Lockhart Mark E,Smith Andrew D. Fatty Liver Disease: Artificial Intelligence Takes on the Challenge.[J]. Radiology,2020,295(2).[50]Wood Edward H,Korot Edward,Storey Philip P,Muscat Stephanie,Williams George A,Drenser Kimberly A. The retina revolution: signaling pathway therapies, genetic therapies, mitochondrial therapies, artificial intelligence.[J]. Current opinion in ophthalmology,2020,31(3).[51]Ho Dean,Quake Stephen R,McCabe Edward R B,Chng Wee Joo,Chow Edward K,Ding Xianting,Gelb Bruce D,Ginsburg Geoffrey S,Hassenstab Jason,Ho Chih-Ming,Mobley William C,Nolan Garry P,Rosen Steven T,Tan Patrick,Yen Yun,Zarrinpar Ali. Enabling Technologies for Personalized and Precision Medicine.[J]. Trends in biotechnology,2020,38(5).[52]Fischer Andreas M,Varga-Szemes Akos,van Assen Marly,Griffith L Parkwood,Sahbaee Pooyan,Sperl Jonathan I,Nance John W,Schoepf U Joseph. Comparison of Artificial Intelligence-Based Fully Automatic Chest CT Emphysema Quantification to Pulmonary Function Testing.[J]. AJR. American journal ofroentgenology,2020,214(5).[53]Moore William,Ko Jane,Gozansky Elliott. Artificial Intelligence Pertaining to Cardiothoracic Imaging and Patient Care: Beyond Image Interpretation.[J]. Journal of thoracic imaging,2020,35(3).[54]Hwang Eui Jin,Park Chang Min. Clinical Implementation of Deep Learning in Thoracic Radiology: Potential Applications and Challenges.[J]. Korean journal of radiology,2020,21(5).[55]Mateen Bilal A,David Anna L,Denaxas Spiros. Electronic Health Records to Predict Gestational Diabetes Risk.[J]. Trends in pharmacological sciences,2020,41(5).[56]Yao Xiang,Mao Ling,Lv Shunli,Ren Zhenghong,Li Wentao,Ren Ke. CT radiomics features as a diagnostic tool for classifying basal ganglia infarction onset time.[J]. Journal of the neurological sciences,2020,412.[57]van Assen Marly,Banerjee Imon,De Cecco Carlo N. Beyond the Artificial Intelligence Hype: What Lies Behind the Algorithms and What We Can Achieve.[J]. Journal of thoracic imaging,2020,35 Suppl 1.[58]Guzik Tomasz J,Fuster Valentin. Leaders in Cardiovascular Research: Valentin Fuster.[J]. Cardiovascular research,2020,116(6).[59]Fischer Andreas M,Eid Marwen,De Cecco Carlo N,Gulsun Mehmet A,van Assen Marly,Nance John W,Sahbaee Pooyan,De Santis Domenico,Bauer Maximilian J,Jacobs Brian E,Varga-Szemes Akos,Kabakus Ismail M,Sharma Puneet,Jackson Logan J,Schoepf U Joseph. Accuracy of an Artificial Intelligence Deep Learning Algorithm Implementing a Recurrent Neural Network With Long Short-term Memory for the Automated Detection of Calcified Plaques From Coronary Computed Tomography Angiography.[J]. Journal of thoracic imaging,2020,35 Suppl 1.[60]Ghosh Adarsh,Kandasamy Devasenathipathy. Interpretable Artificial Intelligence: Why and When.[J]. AJR. American journal of roentgenology,2020,214(5).[61]M.Rosario González-Rodríguez,M.Carmen Díaz-Fernández,Carmen Pacheco Gómez. Facial-expression recognition: An emergent approach to the measurement of tourist satisfaction through emotions[J]. Telematics and Informatics,2020,51.[62]Ru-Xi Ding,Iván Palomares,Xueqing Wang,Guo-Rui Yang,Bingsheng Liu,Yucheng Dong,Enrique Herrera-Viedma,Francisco Herrera. Large-Scale decision-making: Characterization, taxonomy, challenges and future directions from an Artificial Intelligence and applications perspective[J]. Information Fusion,2020,59.[63]Abdulrhman H. Al-Jebrni,Brendan Chwyl,Xiao Yu Wang,Alexander Wong,Bechara J. Saab. AI-enabled remote and objective quantification of stress at scale[J]. Biomedical Signal Processing and Control,2020,59.[64]Gillian Thomas,Elizabeth Eisenhauer,Robert G. Bristow,Cai Grau,Coen Hurkmans,Piet Ost,Matthias Guckenberger,Eric Deutsch,Denis Lacombe,Damien C. Weber. The European Organisation for Research and Treatment of Cancer, State of Science in radiation oncology and priorities for clinical trials meeting report[J]. European Journal of Cancer,2020,131.[65]Muhammad Asif. Are QM models aligned with Industry 4.0? A perspective on current practices[J]. Journal of Cleaner Production,2020,258.[66]Siva Teja Kakileti,Himanshu J. Madhu,Geetha Manjunath,Leonard Wee,Andre Dekker,Sudhakar Sampangi. Personalized risk prediction for breast cancer pre-screening using artificial intelligence and thermal radiomics[J]. Artificial Intelligence In Medicine,2020,105.[67]. Evaluation of Payer Budget Impact Associated with the Use of Artificial Intelligence in Vitro Diagnostic, Kidneyintelx, to Modify DKD Progression:[J]. American Journal of Kidney Diseases,2020,75(5).[68]Rohit Nishant,Mike Kennedy,Jacqueline Corbett. Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda[J]. International Journal of Information Management,2020,53.[69]Hoang Nguyen,Xuan-Nam Bui. Soft computing models for predicting blast-induced air over-pressure: A novel artificial intelligence approach[J]. Applied Soft Computing Journal,2020,92.[70]Benjamin S. Hopkins,Aditya Mazmudar,Conor Driscoll,Mark Svet,Jack Goergen,Max Kelsten,Nathan A. Shlobin,Kartik Kesavabhotla,Zachary A Smith,Nader S Dahdaleh. Using artificial intelligence (AI) to predict postoperative surgical site infection: A retrospective cohort of 4046 posterior spinal fusions[J]. Clinical Neurology and Neurosurgery,2020,192.[71]Mei Yang,Runze Zhou,Xiangjun Qiu,Xiangfei Feng,Jian Sun,Qunshan Wang,Qiufen Lu,Pengpai Zhang,Bo Liu,Wei Li,Mu Chen,Yan Zhao,Binfeng Mo,Xin Zhou,Xi Zhang,Yingxue Hua,Jin Guo,Fangfang Bi,Yajun Cao,Feng Ling,Shengming Shi,Yi-Gang Li. Artificial intelligence-assisted analysis on the association between exposure to ambient fine particulate matter and incidence of arrhythmias in outpatients of Shanghai community hospitals[J]. Environment International,2020,139.[72]Fatemehalsadat Madaeni,Rachid Lhissou,Karem Chokmani,Sebastien Raymond,Yves Gauthier. Ice jam formation, breakup and prediction methods based on hydroclimatic data using artificial intelligence: A review[J]. Cold Regions Science and Technology,2020,174.[73]Steve Chukwuebuka Arum,David Grace,Paul Daniel Mitchell. A review of wireless communication using high-altitude platforms for extended coverage and capacity[J]. Computer Communications,2020,157.[74]Yong-Hong Kuo,Nicholas B. Chan,Janny M.Y. Leung,Helen Meng,Anthony Man-Cho So,Kelvin K.F. Tsoi,Colin A. Graham. An Integrated Approach of Machine Learning and Systems Thinking for Waiting Time Prediction in an Emergency Department[J]. International Journal of Medical Informatics,2020,139.[75]Matteo Terzi,Gian Antonio Susto,Pratik Chaudhari. Directional adversarial training for cost sensitive deep learning classification applications[J]. Engineering Applications of Artificial Intelligence,2020,91.[76]Arman Kilic. Artificial Intelligence and Machine Learning in Cardiovascular Health Care[J]. The Annals of Thoracic Surgery,2020,109(5).[77]Hossein Azarmdel,Ahmad Jahanbakhshi,Seyed Saeid Mohtasebi,Alfredo Rosado Mu?oz. Evaluation of image processing technique as an expert system in mulberry fruit grading based on ripeness level using artificial neural networks (ANNs) and support vector machine (SVM)[J]. Postharvest Biology and Technology,2020,166.[78]Wafaa Wardah,Abdollah Dehzangi,Ghazaleh Taherzadeh,Mahmood A. Rashid,M.G.M. Khan,Tatsuhiko Tsunoda,Alok Sharma. Predicting protein-peptide binding sites with a deep convolutional neural network[J]. Journal of Theoretical Biology,2020,496.[79]Francisco F.X. Vasconcelos,Róger M. Sarmento,Pedro P. Rebou?as Filho,Victor Hugo C. de Albuquerque. Artificial intelligence techniques empowered edge-cloud architecture for brain CT image analysis[J]. Engineering Applications of Artificial Intelligence,2020,91.[80]Masaaki Konishi. Bioethanol production estimated from volatile compositions in hydrolysates of lignocellulosic biomass by deep learning[J]. Journal of Bioscience and Bioengineering,2020,129(6).人工智能英文参考文献三：[81]J. Kwon,K. Kim. Artificial Intelligence for Early Prediction of Pulmonary Hypertension Using Electrocardiography[J]. Journal of Heart and Lung Transplantation,2020,39(4).[82]C. Maathuis,W. Pieters,J. van den Berg. Decision support model for effects estimation and proportionality assessment for targeting in cyber operations[J]. Defence Technology,2020.[83]Samer Ellahham. Artificial Intelligence in Diabetes Care[J]. The American Journal of Medicine,2020.[84]Yi-Ting Hsieh,Lee-Ming Chuang,Yi-Der Jiang,Tien-Jyun Chang,Chung-May Yang,Chang-Hao Yang,Li-Wei Chan,Tzu-Yun Kao,Ta-Ching Chen,Hsuan-Chieh Lin,Chin-Han Tsai,Mingke Chen. Application of deep learning image assessment software VeriSee? for diabetic retinopathy screening[J]. Journal of the Formosan Medical Association,2020.[85]Emre ARTUN,Burak KULGA. Selection of candidate wells for re-fracturing in tight gas sand reservoirs using fuzzy inference[J]. Petroleum Exploration and Development Online,2020,47(2).[86]Alberto Arenal,Cristina Armu?a,Claudio Feijoo,Sergio Ramos,Zimu Xu,Ana Moreno. Innovation ecosystems theory revisited: The case of artificial intelligence in China[J]. Telecommunications Policy,2020.[87]T. Som,M. Dwivedi,C. Dubey,A. Sharma. Parametric Studies on Artificial Intelligence Techniques for Battery SOC Management and Optimization of Renewable Power[J]. Procedia Computer Science,2020,167.[88]Bushra Kidwai,Nadesh RK. Design and Development of Diagnostic Chabot for supporting Primary Health Care Systems[J]. Procedia Computer Science,2020,167.[89]Asl? Bozda?,Ye?im Dokuz,?znur Begüm G?k?ek. Spatial prediction of PM 10 concentration using machine learning algorithms in Ankara, Turkey[J]. Environmental Pollution,2020.[90]K.P. Smith,J.E. Kirby. Image analysis and artificial intelligence in infectious disease diagnostics[J]. Clinical Microbiology and Infection,2020.[91]Alklih Mohamad YOUSEF,Ghahfarokhi Payam KAVOUSI,Marwan ALNUAIMI,Yara ALATRACH. Predictive data analytics application for enhanced oil recovery in a mature field in the Middle East[J]. Petroleum Exploration and Development Online,2020,47(2).[92]Omer F. Ahmad,Danail Stoyanov,Laurence B. Lovat. Barriers and pitfalls for artificial intelligence in gastroenterology: Ethical and regulatory issues[J]. Techniques and Innovations in Gastrointestinal Endoscopy,2020,22(2).[93]Sanne A. Hoogenboom,Ulas Bagci,Michael B. Wallace. Artificial intelligence in gastroenterology. The current state of play and the potential. How will it affect our practice and when?[J]. Techniques and Innovations in Gastrointestinal Endoscopy,2020,22(2).[94]Douglas K. Rex. Can we do resect and discard with artificial intelligence-assisted colon polyp “optical biopsy?”[J]. Techniques and Innovations in Gastrointestinal Endoscopy,2020,22(2).[95]Neal Shahidi,Michael J. Bourke. Can artificial intelligence accurately diagnose endoscopically curable gastrointestinal cancers?[J]. Techniques and Innovations in Gastrointestinal Endoscopy,2020,22(2).[96]Michael Byrne. Artificial intelligence in gastroenterology[J]. Techniques and Innovations in Gastrointestinal Endoscopy,2020,22(2).[97]Piet C. de Groen. Using artificial intelligence to improve adequacy of inspection in gastrointestinal endoscopy[J]. Techniques and Innovations in Gastrointestinal Endoscopy,2020,22(2).[98]Robin Zachariah,Andrew Ninh,William Karnes. Artificial intelligence for colon polyp detection: Why should we embrace this?[J]. Techniques and Innovations in Gastrointestinal Endoscopy,2020,22(2).[99]Alexandra T. Greenhill,Bethany R. Edmunds. A primer of artificial intelligence in medicine[J]. Techniques and Innovations in Gastrointestinal Endoscopy,2020,22(2).[100]Tomohiro Tada,Toshiaki Hirasawa,Toshiyuki Yoshio. The role for artificial intelligence in evaluation of upper GI cancer[J]. Techniques and Innovations in Gastrointestinal Endoscopy,2020,22(2).[101]Yahui Jiang,Meng Yang,Shuhao Wang,Xiangchun Li,Yan Sun. Emerging role of deep learning‐based artificial intelligence in tumor pathology[J]. Cancer Communications,2020,40(4).[102]Kristopher D. Knott,Andreas Seraphim,Joao B. Augusto,Hui Xue,Liza Chacko,Nay Aung,Steffen E. Petersen,Jackie A. Cooper,Charlotte Manisty,Anish N. Bhuva,Tushar Kotecha,Christos V. Bourantas,Rhodri H. Davies,Louise A.E. Brown,Sven Plein,Marianna Fontana,Peter Kellman,James C. Moon. The Prognostic Significance of Quantitative Myocardial Perfusion: An Artificial Intelligence–Based Approach Using Perfusion Mapping[J]. Circulation,2020,141(16).[103]Muhammad Asad,Ahmed Moustafa,Takayuki Ito. FedOpt: Towards Communication Efficiency and Privacy Preservation in Federated Learning[J]. Applied Sciences,2020,10(8).[104]Wu Wenzhi,Zhang Yan,Wang Pu,Zhang Li,Wang Guixiang,Lei Guanghui,Xiao Qiang,Cao Xiaochen,Bian Yueran,Xie Simiao,Huang Fei,Luo Na,Zhang Jingyuan,Luo Mingyan. Psychological stress of medical staffs during outbreak of COVID-19 and adjustment strategy.[J]. Journal of medical virology,2020.[105]. Eyenuk Fulfills Contract for Artificial Intelligence Grading of Retinal Images[J]. Telecomworldwire,2020.[106]Kim Tae Woo,Duhachek Adam. Artificial Intelligence and Persuasion: A Construal-Level Account.[J]. Psychological science,2020,31(4).[107]McCall Becky. COVID-19 and artificial intelligence: protecting health-care workers and curbing the spread.[J]. The Lancet. Digital health,2020,2(4).[108]Alca?iz Mariano,Chicchi Giglioli Irene A,Sirera Marian,Minissi Eleonora,Abad Luis. [Autism spectrum disorder biomarkers based on biosignals, virtual reality and artificial intelligence].[J]. Medicina,2020,80 Suppl 2.[109]Cong Lei,Feng Wanbing,Yao Zhigang,Zhou Xiaoming,Xiao Wei. Deep Learning Model as a New Trend in Computer-aided Diagnosis of Tumor Pathology for Lung Cancer.[J]. Journal of Cancer,2020,11(12).[110]Wang Fengdan,Gu Xiao,Chen Shi,Liu Yongliang,Shen Qing,Pan Hui,Shi Lei,Jin Zhengyu. Artificial intelligence system can achieve comparable results to experts for bone age assessment of Chinese children with abnormal growth and development.[J]. PeerJ,2020,8.[111]Hu Wenmo,Yang Huayu,Xu Haifeng,Mao Yilei. Radiomics based on artificial intelligence in liver diseases: where we are?[J]. Gastroenterology report,2020,8(2).[112]Batayneh Wafa,Abdulhay Enas,Alothman Mohammad. Prediction of the performance of artificial neural networks in mapping sEMG to finger joint angles via signal pre-investigation techniques.[J]. Heliyon,2020,6(4).[113]Aydin Emrah,Türkmen ?nan Utku,Namli G?zde,?ztürk ?i?dem,Esen Ay?e B,Eray Y Nur,Ero?lu Egemen,Akova Fatih. A novel and simple machine learning algorithm for preoperative diagnosis of acute appendicitis in children.[J]. Pediatric surgery international,2020.[114]Ellahham Samer. Artificial Intelligence in Diabetes Care.[J]. The Americanjournal of medicine,2020.[115]David J. Winkel,Thomas J. Weikert,Hanns-Christian Breit,Guillaume Chabin,Eli Gibson,Tobias J. Heye,Dorin Comaniciu,Daniel T. Boll. Validation of a fully automated liver segmentation algorithm using multi-scale deep reinforcement learning and comparison versus manual segmentation[J]. European Journal of Radiology,2020,126.[116]Binjie Fu,Guoshu Wang,Mingyue Wu,Wangjia Li,Yineng Zheng,Zhigang Chu,Fajin Lv. Influence of CT effective dose and convolution kernel on the detection of pulmonary nodules in different artificial intelligence software systems: A phantom study[J]. European Journal of Radiology,2020,126.[117]Georgios N. Kouziokas. A new W-SVM kernel combining PSO-neural network transformed vector and Bayesian optimized SVM in GDP forecasting[J]. Engineering Applications of Artificial Intelligence,2020,92.[118]Qingsong Ruan,Zilin Wang,Yaping Zhou,Dayong Lv. A new investor sentiment indicator ( ISI ) based on artificial intelligence: A powerful return predictor in China[J]. Economic Modelling,2020,88.[119]Mohamed Abdel-Basset,Weiping Ding,Laila Abdel-Fatah. The fusion of Internet of Intelligent Things (IoIT) in remote diagnosis of obstructive Sleep Apnea: A survey and a new model[J]. Information Fusion,2020,61.[120]Federico Caobelli. Artificial intelligence in medical imaging: Game over for radiologists?[J]. European Journal of Radiology,2020,126.以上就是关于人工智能参考文献的分享，希望对你有所帮助。

职称英语理工类A级-22(总分：100.00，做题时间：90分钟)一、{{B}}第1部分：词汇选项{{/B}}(总题数：15，分数：15.00)1.The city has decided to do away with all the old buildings in its center.（分数：1.00）A.get rid of √B.set upC.repairD.paint解析：[解析] 本题考查的是对短语的认知。

这句话的意思是：这个城市决定拆除市中心的所有旧建筑。

本题考察对词组do away with的掌握，意为废除、去掉。

如：I have to do away with my bad habit．我得改掉我的坏习惯。

选项中A．get rid of除去；B．set up设立；C．repair修理；D．paint油漆。

因此选A．get rid of，例句：How can you get rid of this oxide coating?你们该怎样除去这些氧化性涂料?2.The soldier displayed remarkable courage in the battle.（分数：1.00）A.placedB.showed √C.pointedD.decided解析：[解析] 本题考查的是对动词的认知。

这句话的意思：是战士们在战争中表现出非凡的勇气。

本题考察对动词词汇的了解，A．place放置；B．show显示；C．point指引；D．decide决定。

句中语态是一般过去时，谓语应用动词的过去式。

displayed意思为表现，故选B。

3.He is certain that the dictionary is just what I want.（分数：1.00）A.sure √B.angryC.doubtfulD.worried解析：[解析] 本题考查的是对形容词的认知。

这句话的意思是：他很确定这个字典就是我想要的。

安装指南SOLIDWORKS PDM 2020/SOLIDWORKS Manage 2020 /Visualize内容法律声明 (8)1SOLIDWORKS PDM和SOLIDWORKS Manage安装指南 (11)2安装概述 (13)必要的安装组件 (14)可选安装组件（仅对于SOLIDWORKS PDM Professional） (16)SOLIDWORKS PDM调用情形 (16)系统要求 (19)安装摘要 (19)安装帮助 (20)3安装和配置SQL Server (21)支持SQL Server2016、2017和2019 (21)安装SQL Server2016、2017或2019 (22)安装SQL Server2016、2017或2019之前 (22)执行SQL Server2016、2017或2019安装 (22)安装SQL Server2016、2017或2019之后 (25)验证SQL Server2016、2017或2019安装 (25)升级到SQL Server2016、2017或2019 (25)执行SQL Server2016、2017或2019的升级 (26)升级到SQL Server2016、2017或2019之后 (28)SQL Server2014支持 (28)安装SQL Server2014 (28)安装SQL Server2014之前 (28)执行SQL Server2014安装 (29)安装SQL Server2014之后 (33)验证SQL Server2014安装 (33)升级到SQL Server2014 (33)向SQL Server2014升级 (33)升级到SQL Server2014之后 (35)SQL Server疑难解答 (36)客户端不能处理文件库 (36)SOLIDWORKS PDM管理功能失败 (36)SOLIDWORKS PDM不能连接到服务器 (36)更改SQL Server登录帐户 (37)创建新的SQL登录帐户 (37)对SOLIDWORKS PDM存档使用新的SQL登录名 (37)向SQL用户授予访问现有SOLIDWORKS PDM文件库数据库的db_owner权限 (38)SQL权限不足 (39)4安装和配置SQL Server Express (40)安装和管理SQL Server2014Express (40)安装SQL Server2014Express之前 (40)安装SQL Server Express数据库引擎 (40)安装SQL Server2014Express之后 (44)验证SQL Server2014Express安装 (45)将SQL Server Express2014升级到SQL Server2014 (45)5安装SOLIDWORKS PDM (46)下载安装介质 (47)通过SOLIDWORKS安装管理程序启动安装 (47)通过InstallShield向导启动安装 (47)安装SOLIDWORKS PDM数据库服务器 (48)安装数据库服务器之前 (49)执行数据库服务器安装 (49)安装SOLIDWORKS PDM存档服务器 (50)安装存档服务器之前 (51)执行存档服务器安装 (51)为客户端/服务器通信打开端口 (55)在WAN环境中添加存档服务器 (56)安装和配置SolidNetWork许可 (57)安装SolidNetWork License Manager (57)激活SolidNetWork许可 (58)在防火墙环境中使用SolidNetWork许可服务器 (59)SolidNetWork许可管理 (59)许可 (62)安装SOLIDWORKS PDM Web2（仅限SOLIDWORKS PDM Professional） (66)安装Web2之前 (66)执行Web2Server安装 (70)在运行Web2的IIS服务器上创建库视图 (70)配置SOLIDWORKS PDM Web2 (71)安装SOLIDWORKS PDM Web API服务器（仅限SOLIDWORKS PDM Professional） (79)执行Web API服务器安装 (80)安装SOLIDWORKS PDM客户端 (80)安装客户端之前 (81)使用安装向导安装客户端 (82)安装eDrawings (84)将SOLIDWORKS PDM客户端/服务器从Standard升级到Professional (84)启用日志记录以疑难解答安装问题 (84)创建SOLIDWORKS PDM客户端管理映像 (85)使用Active Directory调用客户端 (85)调用SOLIDWORKS PDM时激活日志记录 (87)执行脚本化SOLIDWORKS PDM无声安装 (87)6使用SOLIDWORKS安装管理程序 (91)SOLIDWORKS PDM (91)PDM服务器组件列表 (92)了解PDM客户端之间的区别 (93)安装PDM服务器之前 (94)使用SLDIM安装PDM服务器 (94)使用SLDIM安装PDM客户端 (96)7创建和分发文件库视图 (97)库的生成 (97)先决条件 (97)添加存档服务器 (98)登录存档服务器 (98)生成库 (99)为文件库配置SolidNetWork许可服务器 (104)使用视图设置向导创建文件库视图 (104)启用带Windows防火墙广播 (107)多个用户配置文件使用共享文件库视图 (107)在终端服务器上使用SOLIDWORKS PDM (108)创建文件库视图设置文件 (109)脚本化文件库视图设置 (109)使用Microsoft Windows Active Directory分发文件库视图 (110)查找SOLIDWORKS PDM库ID (111)接收分发的文件库视图 (112)在WAN环境中分发文件库视图 (112)手动配置向SOLIDWORKS PDM客户端公布的存档服务器 (112)手动指定SOLIDWORKS PDM设置组策略 (113)SOLIDWORKS PDM设置策略选项 (113)8将库从Standard升级到Professional (117)将SolidNetWork许可从Standard升级到Professional (117)激活Professional许可 (118)升级Standard库 (119)将SOLIDWORKS PDM客户端/服务器从Standard升级到Professional (119)升级文件库之后 (119)9配置内容搜索（仅对于SOLIDWORKS PDM Professional） (120)内容搜索概述 (120)建议的计算机配置 (121)将Windows搜索用于内容搜索 (121)安装Windows搜索服务 (122)在Windows7上启用Windows搜索服务 (122)在Windows Server2016和更高版本上启用Windows搜索服务 (122)设置Windows搜索 (122)对Windows搜索的存档进行索引 (123)核实Microsoft Indexing Service安装 (123)监控和调整Microsoft Indexing Service (123)索引SOLIDWORKS PDM Professional文件库存档 (124)在非SQL Server系统上配置索引服务 (125)使用索引服务器名更新文件库数据库 (125)更改数据库服务器登录帐户 (125)更改SQL Server登录帐户 (126)添加索引服务器过滤器 (126)压缩的存档(gzip)过滤器 (127)管理文件库索引目录 (127)删除文件库索引 (127)10备份和还原文件库 (129)备份文件库数据库 (129)备份SOLIDWORKS PDM主数据库 (130)备份存档服务器设置 (130)备份存档文件 (131)安排数据库备份时间 (131)启动SQL Server代理 (131)设置数据库备份的维护计划（仅限SOLIDWORKS PDM Professional） (132)还原文件库 (134)还原SQL Server文件库数据库 (134)核实ConisioMasterDb还原 (135)还原存档服务器和文件库存档 (135)11升级SOLIDWORKS PDM (136)关于Enterprise PDM升级 (136)升级之前 (136)确定当前版本 (137)确定已经应用了哪些更新 (138)升级存档服务器 (138)升级数据库服务器 (138)安装或升级SolidNetWork License Manager (139)升级文件库 (140)升级文件库数据库 (140)升级文件库档案 (141)在SOLIDWORKS PDM中升级Toolbox (147)12升级SOLIDWORKS文件 (149)升级SOLIDWORKS文件 (149)所需升级实用程序软件 (150)系统要求 (150)安装文件版本升级实用程序 (151)准备升级 (151)选取版本设定 (152)生成文件的新版本 (153)覆盖文件的现有版本 (156)进行尝试性文件升级 (164)运行升级实用程序 (164)生成和使用工作指南文件 (165)完成被中断的升级 (166)升级之后 (166)升级日志的文件名格式 (167)管理备份文件 (167)备份文件生成 (167)从备份恢复未正确升级的版本 (168)13其它配置 (169)管理SQL事务日志大小 (169)更改到简单恢复模式 (169)减少事务日志的大小 (169)将SOLIDWORKS PDM配置为仅使用IP地址进行通信 (170)更新存档服务器以使用IP地址进行通信 (170)更新SQL服务器以使用IP地址进行通信 (170)更新SOLIDWORKS PDM客户端以使用IP地址进行通信 (170)核实IP地址通信 (171)将服务器组件移到另一个系统 (171)将文件复制到新服务器 (171)配置已经移动的SQL文件库数据库 (172)移动SolidNetWork License Manager (173)移动SOLIDWORKS PDM数据库服务器 (173)配置已移动的存档服务器 (173)更新客户端注册表项 (174)更新复制设定（仅对于SOLIDWORKS PDM Professional） (175)更新管理设置（仅限SOLIDWORKS PDM Professional） (175)核实服务器的移动 (176)14安装SOLIDWORKS Manage Professional (177)SOLIDWORKS Manage服务器组件列表 (177)SOLIDWORKS Manage客户端类型和许可证 (178)SOLIDWORKS Manage的先决条件和系统要求 (179)使用SOLIDWORKS安装管理程序来安装SOLIDWORKS Manage (180)安装SOLIDWORKS Manage服务器 (180)安装SOLIDWORKS Manage客户端 (180)编辑SOLIDWORKS Manage配置文件 (181)15配置SOLIDWORKS Manage Professional和Microsoft IIS (182)首次登录到SOLIDWORKS Manage (182)使用SOLIDWORKS PDM的SOLIDWORKS Manage插件 (185)使用SOLIDWORKS的SOLIDWORKS Manage插件 (185)配置IIS (185)验证IIS文件服务器 (186)IIS故障排除 (187)法律声明©1995-2019,Dassault Systemes SolidWorks Corporation属于Dassault Systèmes 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大规模科研项目越来越无法由一个机构独立完成而必须由来自不同机构的人员和资源协作完成他们共同形成所谓的虚拟组织这些组织是结构化的不同成员应该具有不同的权限网格是一种重要的支持虚拟组织的系统但目前网格系统只包含基础的安全服务可扩展性较差对虚拟组织的支持在规模和复杂度上资源管理者只能根据用户在虚拟组织中的身份管理用户权限Shi bbol et h提供跨域的单点登录并采用基于属性的授权框架为网格中碰到的问题提供了很好的解决方案虚拟组织试图用这种方法来取代用户身份使资源管理者不必关心虚拟组织中的所有用户而只需知道他们的属性即可如数据分析者软件开发者等G r i dShi b项目的目标就是整合网格与高校校园网络基础设施使之更具拓展性和灵活性G r i dShi b项目是由N SF中间件项目NM I NS F Mi d d l e wa r e I ni t i at i ve提供100万美元资助由美国国家超级计算应用中心N CSA N at i onal Cent er of Supercom put i ng牵头还包括来自芝加哥大学美国阿尔贡国家实验室Shi bbol et h开发组的成员目的是整合N S F资助的两大重要软件Int er net2的Shi bbol et h和网格软件G l obus 工具包最著名的网格开源软件由芝加哥大学和美国阿尔贡国家实验室共同开发简称G T实现利用这两种软件构建的系统间的互通该项目于2004年12月1日启动为期两年项目成立一年多以来已经实现了基本的系统间互通使基于GT的网格系统可以从Shi bbol et h服务获取网格用户的属性信息并基于这些信息进行访问控制项目实施的第二年将侧重于提高现有服务的性能关注于解决高层次管理问题如名字空间联盟的管理信任配置管理及网格与Shi bbol et h组件元数据管理等G r i dShi b使网格虚拟组织和高等教育组织间可以进行安全的属性信息交换它为GT提供了基于属性的授权完整的G r i dShi b工具包由G T和Shi bbol et h下的两个插件组成该软件支持推和拉两种操作模式在推模式下用户预先提供其属性在初始阶段获取属性信息并报告给网格服务提供者在拉模式下当用户得到验证后网格服务提供者通过反向信道交换的方式从用户所在的管理域获取用户属性信息在这两种情况下网格服务提供者会根据其获取的用户属性信息进行授权目前G r i dShi b项目已完成的工作包括X.509标准与SA M L标准的整合以及GT授权框架X.509标准与SA M L标准的整合包括三个方面分别是G T4.0插件Shi bbol et h1.3插件及属性交换过程普通的GT系统采用的是X.509安全标准而Sh i bb ol e t h系统采用的是OA SI S的SA M L安全标准两个插件解决了两种标准间的转换问题现在GT被越来越多的系统网格项目采用这些网格项目有不同的特征采用不同的技术与机制GT必须建立能够灵活支持用户定义的格式和使用模式尤其是在属性与授权策略方面GT授权框架采用一致的方式处理不同的机制并综合不同来源的授权结果形成对每次服务访问的决策目前的授权框架只是一个简单的实现不完全支持基于属性的授权也不支持细粒度的授权功能完整的授权框架正在开发中并将出现在G T的下一个重要版本G T4.2中G ri dShi b项目已发布了测试版本并于2005年12月实现了在访问网格A ccessG ri d上的应用2006年G ri dShi b计划实现Shi bbol et h采用SA M L与网格安全标准X.509的无缝整合并实现对在线网格CA认证的整合未来其研究的内容将包括身份管理服务I d e n t i t yProvi der基于数据库名称映射设计并开发名称映射管理工具交互式名称注册服务元数据仓库及管理工具等编译欧海峰INTE RNET2的网格探求大规模项目协作新模式G rid S h ib让校园网更具拓展性学生通过NC SA上的网格共享资源学习多媒体技术研究下一代互联网40中国教育网络2006.1-2。

●提供廣泛的訓練及學習函式●提供Simulink的類神經網路block●可自動將MATLAB產生的類神經網路物件轉成Simulink 模型●提供前處理及後處理函式改善類神經網路訓練及效能●提供視覺化函式更容易瞭解類神經網路之效能(Preceptron)、倒傳遞網路(Backpropagation)、自組織映射網路(SOM)、徑向基網路(Radial Basis Network)、學習向量量化網路(LVQN)..等.◎ Curve Fitting Toolbox(曲線契合工具箱)產品特色●可透過圖形化使用者介面或指令列操作各種功能●可作資料前置處理的例行程序設定，例如資料排序、分割、平滑化、及除去界外值(outlier)等●擁有線性或非線性參數契合模型的龐大的函式庫，與最佳化的起始點(starting points)以及非線性模型參數的解題器●多樣的線性和非線性契合方法，包括最小平方法、加權最小平方法、或強韌契合程序(robustfitting procedures) (上述全部支援限制係數範圍或不限制係數範圍的功能)●客製化的線性或非線性模型發展●使用Splines或內插法(interpolants)進行非參數(Nonparametric)契合●支援內插法、外插法、微分以及積分計算簡介1.功能就是可將客戶的資料畫成圖形，接著提供現有標準的一些數學式子來找出符合這圖形的方程式，例如y=ax+bx^2+cx^3。

2.裡面有提供許多的數學式子，也可讓User自定自己的數學函式，而此工具箱可幫客戶算出數學式子的係數(如a,b,c等)3.提供polynomial, exponential, Fourier, rational等數學model給客戶Note: 此套工具不能作曲面的契合，即是如果客戶要求要作y=ax1+bx2 找出a與b，是不可以的，因為此工具箱只能找出單一變數x的係數。

建議他們購買OP and SP 解決他們的問題。