What's New in ODYSSEE A-Eye 2022.3

Main Functionality

• When installing a new A-Eye, if the folder containing the examples projects already exists, it is proposed to overwrite its content to replace it with the new one.

A-Eye App

This version includes:

• Added “+” (add) and “-” (remove) buttons in the Learning and Prediction tabs to add or remove a line case and complete the fields manually without load a csv file.
• Curve viewer in the Learning or Prediction tab:
  • Possibility to add multiple prediction curves simultaneously in the curve viewer and upload the corresponding exact curves. Curves can be coming from hard disk, learning database, or last prediction cases.
  • Ergonomic improvements in the curve viewer like the plot curves with different colours.
• Now, A-Eye calculates the prediction of multiple XN cases at the same time. Until now, A-Eye was predicting the different XN cases one after another in a loop.
• The cells of Value type are not initialised anymore at 0. A horizontal bar indicates the cell content is empty and can be edited manually.
• Bug Fixes
  • The ODYSSEE A-Eye logo at the top right corner of the main A-Eye App window was partially cut.
  • In the prediction report:
    • There was an overlap between the image and the result matrix.
    • Some images and section titles were cut.
  • In the curve displayer:
    • Number of decimal places field name has been changed to Cursor: Nbr of decimals for better understanding.
    • The curves deleted with the “-” (remove) button remained displayed.

A-Eye Manager

• Added an option to display the company logo and a title at the top right corner of the main A-Eye App window.
• It is now possible to input a negative value to Power parameter for Inverse Distance prediction script.
• The prediction script name now corresponds to the prediction method used.
• Bug Fixes
  • For Inverse Distance method, the number of Neighbors written in the prediction script was shifted by -1 compared with the value specified in the interface.

ODYSSEE Quasar – Solver

• Add a new method for Multiple Layer Perceptron (MLP) named Mechanics Informed Neural Network (MINN) applying a constraint on the Jacobian of the output layer .
• FMU improvement: Use the analytical form of the stiffness matrix when using MINN. For the case of Standard Neural Network (MLP), we use finite difference method to compute this matrix.
• A message is now displayed in the command prompt if the user calls a bad *.qsr file (error in the filename or file not existing in the directory).
• Update/New Quasar functions:
  • For DOE:
    • Add automatically new points in a sample file (X.csv). For simplify the GPNewpoint function, the arguments list has been reduced. GPNewpoint becomes GPAdaptative:

ext("operator","matrix","gpadaptive",X,Xcandidate,gridPts,n_pts,PCA)
The new function gives the same results of the previous one.

• New keyword to generate a “Normal” distribution with a specific mean and variance. To control the space filling, you need to enter Kscale=4 and load csv file File_MeanSTDV containing 2 columns: first the means, second the standard deviation.

ext("doe","meanstddevnormal",Nvariables,Nsites,Neighbours,Seed,Kscale=4,"doe_mstdnormal_sampling",File_MINMAX,rmin,rmax,File_MeanSTDV)

• For regression:
  • ext("mining","effectsqr",Xstd,Y,"regressionQR_"). Add an output file (regressionQR__REGRESSION_COEFFICIENTS.csv) for the effectsqr function.
  • When ARBF interpolation is applied, an output file named out is generated saving the chosen base function and radius.
  • GPNewpoints external function to add samples automatically to the database becomes GPAdaptative external function simplifying the arguments list.

ext("operator", "matrix", "gpadaptive", X, Xcandidate, gridPts, nbr_new_points, nbr_Eigen_vector_PCA)

• A new neural networks function called NN has been implemented for new options integration.

Matrix YN = NN(X, Y, XN, architecture, learning_rate, activation, optimizer, percentage, isshuffled, n_epochs, batch_size, Prefix, "normalize ", method, save_files)

• The arguments of LSTM function are reordered for a best intuitiveness.

Matrix YN = LSTM(X, Y , XN, n_neurons, Tx, batch_size, learning_rate, activation_name, optimizer, normOption, percentage, isShuffled, n_epochs, prefix_file, saveFile)

• For pre-processing:
  • Added the matspline(X,Y,XN,"root.csv") function that will transform a curve or a signal with n points in a curve or signal of m points where m > n.
  • Added a flag argument ext("operator","matrix","order_y_by_row_index_X", X, Y,column=2, flag). If FLAG=0 all columns of Y will be ordered as in X (:,column) else FLAG=1 only the column col of Y will be reordered.
• Added 3 new functions in the Useful_Functions.qsr library:
• Replace function to change a text from start position to an end position.
• Order function to sort by column a Matrix at 1 line (equivalent to a vector).
• addLabelToMatrixFileAndSave function to save a matrix in a file with the asked separator and columns labels wanted.

• Bug Fixes
  • The multiple successive calls of the SVM function generated an error.
  • For the correlation and covariance functions, if matA, and matB tested have not the same size, then an error message is now displayed.