I've added the side coil to the model controller and plant, and the oplev quad to the model controller and plant.  After the megatron wipe, the code now lives in /home/controls/cds/advLigo/src/epics/simLink.  The files are mdc.mdl (controller) and mdp.mdl (plant).  These RCG modules go at 16K with no decimation (no_oversampling=1 in the cdsParameters block) so hopefully will work with the old (16K) timing.

I've loaded many of the filters, there are some eft to do.  These filters are simply copied from the current frontend.  

Next I will port to the SUS module (which talks to the IO chassis).  This means channel names will match with the current system, which will be important when we plug in the RFM.

The .mdl code for the mdc and mdp development modules is finished.  These modules need more filters, and testing.  Probably the most interesting piece left to do is putting in the gains and filters for the oplev model in mdp.  It might be OK to simply ignore oplevs and first test damping of the real optic without them.   However, it shouldn't be hard to get decent numbers for oplevs, add them to the mdp (plant) module, and make sure the mdc/mdp pair is stable.  In mdp, the oplev path starts with the SUSPIT and SUSYAW signals. Kakeru recently completed calibration of the oplevs from oplev cts to radians:   1403  .  From this work we should find the conversion factors from PIT and YAW to oplev counts, without making any new measurements.  (The measurements wouldn't be hard either, if we can't simply pull numbers from a document.)  These factors can be added to mdp as appropriate gains.

I've also copied mdc to a new module, which I've named "sas" to address fears of channel name collisions in the short term, and replaced the cpu-to-cpu connections with ADC and DAC connections.  sas can be the guy for the phase I ETMY test.  When we're happy with mdc/mdp, we hopefully can take the mdc filter file from chans, replace all the "MDC" strings with "SAS", and use it.

/cvs/cds/caltech/target/fb/daqd -c daqdrc

This starts the FB.

Now the dataviewer and DTT work!

 Instead of doing RCG stuff, I went to Millikan to work on data analysis as I couldn't stand the fumes from the construction.  (this morning, 8am) 

the command

tdsavg 5 C1:LSC-PD4_DC_IN1

was causing grievous woe in the cm_step script.  It turned out to fail intermittently at the command line, as did other LSC channels.  (But non-LSC channels seem to be OK.)  So we power cycled c1lsc (we couldn't ssh).

Then we noticed that computers were out of sync again (several timing fields said 16383 in the C0DAQ_RFMNETWORK screen).  We restarted c1iscey, c1iscex, c1lsc, c1susvme1, and c1susvme2.  The timing fields went back to 0.  But the tdsavg command still  intermittently said "ERROR: LDAQ - SendRequest - bad NDS status: 13".

The channel C1:LSC-SRM_OUT16 seems to work with tdsavg every time.

Let us know if you know how to fix this. 

attached plot shows MC_IN1/MC_IN2.  needs work.

This is supposed to be a measurement of the relative gain of the MCL and AO paths in the CM servo. We expect there to

be a more steep slope (ideally 1/f). Somehow the magnitude is very shallow and so the crossover is not stable. Possible

causes? Saturations in the measurement, broken whitening filters, extremely bad delay in the digital system? needs work.

Today Steve and I tried to to capture the image of scattering of light by dust particles on the surface of ETMX using GigE camera. The image ( at gain =100, exposure time = 125000) obtained has been attached. Unlike the previous images, a creepy shape of bright spots was seen. Gautam helped us lock infrared light and see the image. A similar less intense shape was seen. This may be because of the dust on the lens.

Aim: To synchronize data from the captured video and the signal applied to ETMX

In order to correlate the intensity fluctuations of the scattered light with the motion of the test mass, we are planning to use the technique of neural network. For this, we need a synchronised video of scattered light with the signal applied to the test mass. Gautam helped me capture 60sec video of scattering of infrared laser light after ETMX was dithered in PITCH at ~0.2Hz..

I developed a python program to capture the video and convert it into a time series of the sum of pixel values in each frame using OpenCV to see the variation. Initially we had tried the same with green laser light and signal of approximately 11.12Hz. But in order to see the variation clearly, we repeated with a lower frequency signal after locking IR laser today. I have attached the plots that we got below. The first graph gives the intensity fluctuations from the video. The third and fourth graphs are that of transmitted light and the signal applied to ETMX to shake it. Since the video captured using the camera was very noisy and intensity fluctuations in the scattered light had twice the frequency of the signal applied, we captured a video after turning off the laser. The second plot gives the background noise probably from the camera. Since camera noise is very high, it may not be possible to train this data set in neural network.

Since the videos captured consume a lot of memory I haven't uploaded it here. I have uploaded the python code 'sync_plots.py' in github (https://github.com/CaltechExperimentalGravity/GigEcamera/tree/master/Pooja%20Sekhar/PythonCode).

Just to inform, I'm working in optimus to develop python code to train the neural network since it requires a lot of memory.

Aim: To develop a neural network in order to correlate the intensity fluctuations in the scattered light to the angular motion of the test mass. A block diagram of the technique employed is given in Attachment 1.

I have used Keras to implement supervised learning using neural network (NN). Initially I had developed a python code that converts a video (59 sec) of scattered light, after an excitation (sine wave of frequency 0.2 Hz) is applied to ETMX pitch, to image frames (of size 480*720)  and stores the 2D pixel values of 1791 images frames captured into an hdf5 file. This array of shape (1791,36500) is given as an input to the neural network. I have tried to implement regular NN only, not convolution or recurrent NN. I have used sequential model in Keras to do this. I have tried with various number of dense layers and varied the number of nodes in each layer. I got test accuracy of approximately 7% using the following network. There are two dense layers, first one with 750 nodes with a dropout of 0.1 ( 10% of the nodes not used) and second one with 500 nodes. To add nonlinearity to the network, both the layers are given an activation function of tanh. The output layer has 1 node and expects an output of shape (1791,1). This model has been compiled with a loss function of categorical crossentropy, optimizer = RMSprop. We have used these since they have been mostly used in the image analysis examples. Then the model is trained against the dataset of mirror motion. This has been obtained by sampling the cosine wave fit to the mirror motion so that the shapes of the input and output of NN are consistent. I have used a batch size ( number of samples per gradient update) = 32 and epochs (number of times entire dataset passes through NN) = 20. However, using this we got an accuracy of only 7.6%. 

I think that the above technique gives overfitting since dense layers use all the nodes during training apart from giving a dropout. Also, the beam spot moves in the video. So it may be necessary to use convolution NN to extract the information.

The video file can be accesses from this link https://drive.google.com/file/d/1VbXcPTfC9GH2ttZNWM7Lg0RqD7qiCZuA/view.

Gabriele told us that he had used the beam spot motion to train the neural network. Also he informed that GPUs are necessary for this. So we have to figure out a better way to train the network.  

gautam noon 11Jun: This link explains why the straight-up fully connected NN architecture is ill-suited for the kind of application we have in mind. Discussing with Gabriele, he informed us that training on a GPU machine with 1000 images took a few hours. I'm not sure what the CPU/GPU scaling is for this application, but given that he trained for 10000 epochs, and we see that training for 20 epochs on Optimus already takes ~30minutes, seems like a futile exercise to keep trying on CPU machines.

Aim: To calibrate CCD of GigE using LED1050E.

The following table shows some of the specifications for LED1050E as given in Thorlabs datasheet.

 The circuit diagram is given in Attachment 1.

Considering a power supply voltage V_cc = 15V, current I = 20mA & forward voltage of led V_F = 1.25V, resistance in the circuit is calculated as,

R = (V_cc - V_F)/I = 687.5

Attachment 2 gives a plot of resistance (R) vs input voltage (V_cc) when a current of 20mA flows through the circuit. I hope I can proceed with this setup soon.

Today I made the led (1050nm) circuit inside a box as given in my previous elog. Steve drilled a 1mm hole in the box as an aperture for led light.

Resistance (R) used = 665 .

We connected a power supply and IR has been detected using the card.

Later we changed the input voltage and measured the optical power using a powermeter.

Input voltage (Vcc in V)	Optical power
0 (dark reading)	60 nW
15	68 W
18	82.5 W
20	92 W

Since the optical power values are very less, we may need to drill a larger hole.

Now the hole is approximately 7mm from led, therefore aperture angle is approximately 2*tan^-1(0.5/7) = 8deg. From radiometric curve given in the datasheet of LED1050E, most of the power is within 20 deg. So a hole of size 2* tan(10) *7 = 2.5mm may be required.

I have also attached a photo of the led beam spot on the IR detection card.

Aim : To develop a neural network on simulated data.

I developed a python code that generates a 64*64 image of a white Gaussian beam spot at the centre of black background. I gave a sine wave of frequency 0.2Hz that moves the spot vertically (i.e. in pitch). Then I simulated this video at 10 frames/sec for 10 seconds. Then I saved this data into an hdf5 file, reshaped it to a 1D array and gave as input to a neural network. Out of the 100 image frames, 75 were taken as training dataset and 25 as test data. I varied several hyperparameters like learning rate of the optimizer, number of layers, nodes, activation function etc. Finally, I was successful in reducing the mean squared error with the following network model:

• Sequential model of 2 fully connected layers with 256 nodes each and a dropout of 0.1
• loss function = mean squared error, optimizer = RMSprop (learning rate = 0.00001) and activation function that adds nonlinearity = relu
• batch size = 32 and number of epochs = 1000

I have attached the plot of the output of neural network (NN) as well as sine signal applied to simulate the video and their residula error in Attachment 1. The plot of variation in mean squared error (in log scale) as number of epochs increases is given in Attachment 2.

I think this network worked easily since there is no noise in the input. Gautam suggested to try the working of this network on simulated data with a noisy background.

Aim: To measure the optical power from led using a powermeter.

Yesterday Gautam drilled a larger hole of diameter 5mm in the box as an aperture for led (aperture angle is approximately 2*tan^-1(2.5/7) = 39 deg). I repeated the measurements that I had done before (https://nodus.ligo.caltech.edu:8081/40m/13951). The measurents of optical power measured using a powermeter and the corresponding input voltages are listed below.

So we are able to receive optical power close to the value (1.6mW) given in Thorlabs datasheet for LED1050E (https://www.thorlabs.com/drawings/e6da1d5608eefd5c-035CFFE5-C317-209E-7686CA23F717638B/LED1050E-SpecSheet.pdf). I hope we can proceed to BRDF measurements for CCD calibration.

Steve: did you center the LED ?

Yes.

Aim: To find a model that trains the simulated data of Gaussian beam spot moving in a vertical direction by the application of a sinusoidal signal.

All the attachments are in the zip folder.

The simulated video of beam spot motion without noise (amplitude of sinusoidal signal given = 20 pixels) is given in this link https://drive.google.com/file/d/1oCqd0Ki7wUm64QeFxmF3jRQ7gDUnuAfx/view?usp=sharing

I tried several cases:

Case 1:

I added random uniform noise (ranging from 0 to 25.5 i.e. 10% of the maximum pixel value 255) using opencv to 64*64 simulated images made in the last case( https://nodus.ligo.caltech.edu:8081/40m/13972), clipped the pixel values from 0 to 255 & trained using the same network as in the previous elog and it worked well. The variation in mean squared error with epochs is given in Attachment 1 & applied signal and output of the neural network (NN) (magnitude of the signal vs time) as well as the residual error is given in Attachment 2.

Case 2:

I simulated images 128*128 at 10 frames/sec by applying a sine wave of frequency 0.2Hz that moves the beam spot & resized it using opencv to 64*64. Then I trained 300cycles & tested with 1000 cycles with the following sequential model:

(i) Layers and number of nodes in each:

                                             4096 (dropout = 0.1) -> 1024 (dropout = 0.1) -> 512 (dropout = 0.1) -> 256  ->  64 ->  8   ->   1

           Activation :                        selu                   ->                 selu             ->         selu                -> selu ->  selu -> selu -> linear

(ii) loss function = mean squared error ( I used mean squared error to easily comprehend the result. Initially I had tried log(cosh) also but unfortunately I had stopped the run in between when test loss value had no improvement), optimizer = Nadam with default learning rate = 0.002

(iii) batch size = 32, no. of epochs = 400

I have attached the variation in loss function with epochs (Attachment 3). It was found that test loss value increases after ~50 epochs. To avoid overfitting, I added dropout to the layer of 256 nodes in the next model and removed the layer of 4096 nodes.

Case 3: 

Same simulated data as case 2 trained with the following model,

(i) Layers and number of nodes in each:

                                             1024 (dropout = 0.1) -> 512 (dropout = 0.1) -> 256 (dropout = 0.1) ->  64 ->  8   ->   1

           Activation :                              selu             ->         selu                ->              selu ->             selu -> selu -> linear

(ii) changed the learning rate from default value of 0.002 to 0.001. Rest of the hyperparameters same.

The variation in mean squared error in attachment 4  & NN output, applied signal & residual error (zoomed) in attachment 5. Here also test loss value increases after ~65 epochs but this fits better than the previous model as loss value is less.

Case 4:

Since in most of the examples in keras, training dataset was more than test dataset, I tried training 1000 cycles & testing with 300 cycles. The respective plots are attached as attachment 6 & 7. Here also, there is no significant improvement except that the test loss is increasing at a slower rate with epochs as compared to the last case.

Case 5:

Since most of the above cases were like overfitting (https://machinelearningmastery.com/diagnose-overfitting-underfitting-lstm-models/, https://github.com/keras-team/keras/issues/3755) except that test loss is less than train loss value in the beginning , I tried implementing case 4 with the initial model of 2 layers of 256 nodes each but with Nadam optimizer. Respective graphs in attachment 8, 9 & 10(zoomed). The loss value is slightly higher than the previous models as seen from the graph but test & train loss values converge after some epochs.

I have forgot to give ylabel in some of the graphs. It's the magnitude of the applied sine signal to move the beam spot. In most of the cases, the network almost correctly fits the data and test loss value is lower in the initial epochs. I think it's because of the dropout we added in the model & also we are training on the clean dataset.

Aim: To track the motion of beam spot in simulated video.

I simulated a video that moves the beam spot at the centre of the image initially by applying a sinusoidal signal of frequency 0.2Hz and amplitude 1 i.e. it moves maximum by 1 pixel. It can be found in this shared google drive link (https://drive.google.com/file/d/1GYxPbsi3o9W0VXybPfPSigZtWnVn7656/view?usp=sharing). I found a program that uses Kernelized Correlation Filter (KCF) to track object motion from the video. In the program we can initially define the bounding box (rectangle) that encloses the object we want to track in the video or select the bounding box by dragging in GUI platform. Then I saved the bounding box parameters in the program (x & y coordinates of the left corner point, width & height) and plotted the variation in the y coordinates. I have yet to figure out how this tracker works since the program reads 64*64 image frames in video as 480*640 frames with 3 colour channels and frame rate also randomly changes. The plot of the output of this tracking program & the applied signal has been attached below. The output is not exactly sinusoidal because it may not be able to track very slight movement especially at the peaks where the slope = 0.

Aim:  To find a model that trains the simulated data of Gaussian beam spot moving in a vertical direction by the application of a sinusoidal signal. The data also includes random uniform noise ranging from 0 to 10.

All the attachments are in the zip folder.

I simulated images 128*128 at 10 frames/sec by applying a sine wave of frequency 0.2Hz that moves the beam spot, added random uniform noise ranging from 0 to 10 & resized the image frame using opencv to 64*64. 1000 cycles of this data is taken as train & 300 cycles as test data for the following cases. Optimizer = Nadam (learning rate = 0.001), loss function used = mean squared error, batch size = 32,

Case 1:

Model topology:

                         256 (dropout = 0.1)  ->           256 (dropout = 0.1)   ->       1

Activation :             selu                                         selu

Number of epochs = 240.

Variation in loss value of train & test datasets is given in Attachment 1 of the attached zip folder & the applied signal as well as the output of neural network given in Attachments 2 & 3 (zoomed version of 2).

The model fits well but there is no training since test loss is lower than train loss value. I found in several sites that dropout of some of the nodes during training but retaining them during test could be the probable reason for this (https://stackoverflow.com/questions/48393438/validation-loss-when-using-dropout , http://forums.fast.ai/t/validation-loss-lower-than-training-loss/4581 ). So I removed dropout while training next time.

Case 2:

Model topology:

                         256 (dropout = 0.1)  ->           256 (dropout = 0.1)   ->       1

Activation :             selu                                         selu                          linear

Number of epochs = 200.

Variation in loss value of train & test datasets is given in Attachment 4 of the attached zip folder & the applied signal as well as the output of neural network given in Attachments 5 & 6 (zoomed version of 2).

But still no improvement.

Case 3:

I changed the optimizer to Adam and tried with the same model topology & hyperparameters as case 2 with no success (Attachments 7,8 & 9).

Finally I think this is because I'm training & testing on the same data. So I'm now training with the simulated video but moving it by a maximum of 2 pixels only and testing with a video of ETMY that we had captured earlier.

(Gautam, Pooja)

Aim: To develop medm screen for GigE.

Gautam helped me set up the medm screen through which we can interact with the GigE camera. The steps adopted are as follows:

(i) Copied CUST_CAMERA.adl file from the location /opt/rtcds/userapps/release/cds/common/medm/ to /opt/rtcds/caltech/c1/medm/MISC/.

(ii) Made the following changes by opening CUST_CAMERA.adl in text editor.  

• Changed the name of file to "/cvs/cds/rtcds/caltech/c1/medm/MISC/CUST_CAMERA.adl"
• Replaced all occurences of "/ligo/apps/linux-x86_64/camera/bin/" to "/opt/rtcds/caltech/c1/scripts/GigE/SnapPy_pypylon/" & "/ligo/cds/$(site)/$(ifo)/camera/" to "/opt/rtcds/caltech/c1/scripts/GigE/SnapPy_pypylon/"

(iii) Added this .adl file as drop-out menu 'GigE' to VIDEO/LIGHTS section of sitemap (circled in Attachment 1) i.e opened Resource Palette of VIDEO/LIGHTS, clicked on Label/Name/Args & defined macros as CAMERA=C1:CAM-ETMX,CONFIG=C1-CAM-ETMX in Arguments box of Related Display Data dialog box (circled in Attachment 2) that appears. In Related Display Data dialog box, Display label is given as GigE and Display File as ./MISC/CUST_CAMERA.adl

(iv) All the channel names can be found in Jigyasa's elog https://nodus.ligo.caltech.edu:8081/40m/13023

(v) Since the slider (circled in Attachment 3) for pixel sum was not moving, changed the high limit value to 10000000 in PV Limits dialog box. This value is set such that the slider reaches the other end on setting the exposure time to maximum.

(vii) Set the Snapshot channel C1:CAM-ETMX_SNAP to off (very important!). Otherwise we cannot interact with the camera.

(vii) GigE camera gstreamer client is run in tmux session.

Now we can change the exposure time and record a video by specifying the filename and its location using medm screen. However, while recording the video, gstream video laucher of GigE stops or is stuck.

Projector light bulb blown out today.

Aim: To develop a neural network that resolves mirror motion from video.

I had created a python code to find the combination of hyperparameters that trains the neural network. The code (nn_hyperparam_opt.py) is present in the github repo. It's running in cluster since a few days. In the meanwhile I had just tried some combination of hyperparameters.

These give a low loss value of approximately 1e-5 but there is a large error bar for loss value since it fluctuates a lot even after 1500 epochs. This is unclear.

Input: 64*64 image frames of simulated video by applying beam motion sine wave of frequency 0.2Hz and at 10 frames per sec. This input data is given as an hdf5 file.

Train : 100 cycles,  Test: 300 cycles, Optimizer = Nadam (learning rate = 0.001)

Model topology:

                    256       ->      128    ->       1

Activation :        selu     selu           linear

Case 1: batch size = 48, epochs = 1000, loss function = mean squared error

Plots of output predicted by neural network (NN) & input signal has been shown in 1st graph & variation in loss value with epochs in 2nd graph.

Case 2: batch size = 32, epochs = 1500, loss function = mean squared logarithmic error

Plots of output predicted by neural network (NN) & input signal has been shown in 3rd graph & variation in loss value with epochs in 4th graph.

Aim: To develop a neural network that resolves mirror motion from video.

I tried to reduce the overfitting problem in previous neural network by reducing the number of nodes and layers and by varying the learning rate, beta factors (exponential decay rates of moving first and second moments) of Nadam optimizer assuming error of 5% is reasonable.

Input:

32 * 32 image frames (converted to 1d array & pixel values of 0 to 255 normalized) of simulated video by applying sine signal to move beam spot in pitch with frequency 0.2Hz and at 10 frames per second.

Total: 300 cycles ,           Train: 60 cycles,    Validation: 90 cycles,    Test: 150 cycles

Model topology:

                                          Input               -->                  Hidden layer               -->                    Output layer                                  

                                                                                          4 nodes                                              1 node

Activation function:                                  selu                                             linear

Batch size = 32, Number of epochs = 128, loss function = mean squared error

Optimizer: Nadam

Case 1:

Learning rate = 0.00001,    beta_1 = 0.8 (default value in Keras = 0.9),  beta_2 = 0.85 (default value in Keras = 0.999)

Plot of predicted output by neural network, applied input signal & residual error given in 1st attachment.

Case 2:

Changed number of nodes in hidden layer from 4 to 8. All other parameters same.

These plots show that when residual error increases basically the output of neural network has a smaller amplitude compared to the applied signal. This kind of training error is unclear to me.

When beta parameters of optimizer is changed farther from 1, error increases.

Aim: To develop a neural network that resolves mirror motion from video.

Case 1:

Input : Simulated video of beam spot motion in pitch by applying 4 sine  waves of frquencies 0.2, 0.4, 0.1, 0.3 Hz  and amplitude ratios to frame size to be 0.1, 0.04, 0.05, 0.08

The data has been split into train, validation and test datasets and I tried training on neural network with the same model topology & parameters as in my previous elog (https://nodus.ligo.caltech.edu:8081/40m/14070)

The output of NN and residual error have been shown in Attachment 1. This NN model gives a large error for this. So I think we have to increase the number of nodes and learning rate so that we get a lower error value with a single sine wave simulated video ( but not overfitting) and then try training on linear combination of sine waves.

Case 2 :

Normalized the target sine signal of NN so that it ranges from -1 to 1 and then trained on the same neural network as in my previous elog with simulated video created using single sine wave. This gave comparatively lower error (shown in Attachment 2). But if we train using this network, we can get only the frequency of test mass motion but we can't resolve the amount by which test mass moves. So I'm unclear about whether we can use this.

Aim: To develop a neural network that resolves mirror motion from video.

Since error was high for the same input as in my previous elog http://nodus.ligo.caltech.edu:8080/40m/14089

I modified the network topology by tuning the number of nodes, layers and learning rate so that the model fitted the sum of 4 sine waves efficiently, saved weights of the final epoch and then in a different program, loaded saved weights & tested on simulated video that's produced by moving beam spot from the centre of image by sum of 4 sine waves whose frequencies and amplitudes change with time.

Input : Simulated video of beam spot motion in pitch by applying 4 sine  waves of frquencies 0.2, 0.4, 0.1, 0.3 Hz  and amplitude ratios to frame size to be 0.1, 0.04, 0.05, 0.08. This is divided into train (0.4), validation (0.1) and test (0.5).

Model topology:

                                          Input               -->                  Hidden layer               -->                    Output layer                                  

                                                                                          8 nodes                                              1 node

Activation function:                                  selu                                             linear

Batch size = 32, Number of epochs = 128, loss function = mean squared error

Optimizer: Nadam ( learning rate = 0.00001, beta_1 = 0.8, beta_2 = 0.85)

Normalized the target sine signal of NN by dividing by its maximum value.

Plot of predicted output by neural network, applied input signal & residual error given in 1st attachment. These weights of the model in the final epoch have been saved to h5 file and then loaded & tested with simulated data of 4 sine waves with amplitudes and frequencies changing with time from their initial values by random uniform noise ranging from 0 to 0.05. Plot of predicted output by neural network, target signal of sine waves & residual error given in 2nd attachment. The actual signal can be got from predicted output of NN by multiplication with normalization constant used before. However, even though network fits training  & validation sets efficiently, it gives a comparatively large error on test data of varying amplitude & frequency.

Gautam suggested to try training on this noisy data of varying amplitudes and frequencies. The results using the same model of NN is given in Attachment 3. It was found that tuning the number of nodes, layers or learning rate didn't improve fitting much in this case.

Aim: To develop a convolutional neural network that resolves mirror motion from video.

Input : Previous simulated video of beam spot motion in pitch by applying 4 sine  waves of frquencies 0.2, 0.4, 0.1, 0.3 Hz  and amplitude ratios to frame size to be 0.1, 0.04, 0.05, 0.08 where random uniform noise ranging 0.05 has been added to amplitudes and frequencies. This is divided into train (0.4), validation (0.1) and test (0.5).

Model topology:

• Number of filters = 2
• Kernel size = 2
• Size of pooling windows = 2
•                                        ----->         Dense layer of 4 nodes  ---->    Output layer of 1 node 

         Activation:                      selu                                                  linear

Batch size = 32, Number of epochs = 128, loss function = mean squared error

Optimizer: Nadam ( learning rate = 0.00001, beta_1 = 0.8, beta_2 = 0.85)

Plots of CNN output & applied signal given in Attachment 1. The variation in loss value with epochs given in Attachment 2.

This needs to be further analysed with increasing random uniform noise over the pixels and by training CNN on simulated data of varying ampltides and frequencies for sine waves.

C1:PEM-BS_ACC_EAST_Z

MC1     28.21625 kHz
MC2     28.036   kHz
MC3     28.21637 kHz

- Turn off the MC autolocker. Relock the MC with only the acquisition settings; no boosts
  and no RGs. This makes it re-acquire fast. Turn the MC-WFS gain down to 0.001 so that
  it keeps it slowly aligned but does not drift off when you lose lock.

- Use low-ish gain on the FSS. 10 dB lower than nominal is fine.

- Setup the o'scope (100 MHz BW or greater) to do single shot trigger on the MC2 trans.

- Flip FSS sign.

- Quickly flip sign back and waggle common gain to get FSS to stop oscillating. MC
  should relock in seconds.

#!/bin/csh

set ifo = C1
set sus = ${ifo}:SUS-

foreach opt (MC1 MC2 MC3)

  set c = `ezcaread -n ${sus}${opt}_PD_MAX_VAR`
  ezcastep ${sus}${opt}_PD_MAX_VAR +300

  ezcaswitch ${sus}${opt}_ULCOIL OFFSET ON
  ezcawrite ${sus}${opt}_ULCOIL_OFFSET 30000
  sleep 1
  ezcawrite ${sus}${opt}_ULCOIL_OFFSET 0
  sleep 1
  ezcawrite ${sus}${opt}_ULCOIL_OFFSET 30000
  sleep 1
  ezcawrite ${sus}${opt}_LATCH_OFF 0

  ezcawrite ${sus}${opt}_ULCOIL_OFFSET 0
  ezcaswitch ${sus}${opt}_ULCOIL OFFSET OFF

  ezcawrite ${sus}${opt}_PD_MAX_VAR $c

end

echo
date
echo