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Hello, I have a Samba setup on a Solaris 8 box and mapped a smb drive to a Windows 2000 with plain text authentication ( modified W2k Local security policy to send plain text password). When I reboot the system, it prompts me for the password, but doesn't take it. I have to remap the drive, every time I reboot. Is there a fix or work around? Sundar

Hi, Finally I have decided to give go ahead for Samba-LDAP. The questions are 1)I have a system that provides LDAP directory access. Is there anyway, I could just point my SAMBA server to point to that and say 'get the users authenticated'? Or do I have to setup a local LDAP and then add Samba to that. 2) Could my Samba be just a workgroup to configure LDAP with it or does it have to

On Thu, Aug 24, 2017 at 7:07 AM, David Winsemius <dwinsemius at comcast.net> wrote: > > > On Aug 23, 2017, at 10:06 PM, Dhivya Narayanasamy <dhiv.shreya at gmail.com> > wrote: > > > > I have a vibration signal coming accelerometer. I converted this signal > from* > > m/s^2* to *mm/s*. Now I am supposed to predict this vibration signal in R > >

Hi, I am trying to fit a multivariate time series model using DCC GARCH model and forecast it. The data looks like this: > head(datax) x vibration_x Speed 1 2017-05-16 17:53:00 -0.132 421.4189 2 2017-05-16 17:54:00 -0.296 1296.8882 3 2017-05-16 17:55:00 -0.572 0.0000 4 2017-05-16 17:56:00 -0.736 1254.2695 5 2017-05-16 17:57:00 0.000

I have a vibration signal coming accelerometer. I converted this signal from* m/s^2* to *mm/s*. Now I am supposed to predict this vibration signal in R using historical data. (Please see the attached picture of vibration signal). Can I use this vibration signal just like that to the prediction using predictive Models or Do i need to do some processing technique before doing prediction? Are there

Hi, I am doing predictive modelling of Multivariate Time series Data of a Motor in R using various models such as Arima, H2O.Randomforest, glmnet, lm and few other models. I created a function to select a model of our choice and do prediction. Model1 <- function(){ .. return() } Model2 <- function(){ ... return() } Model3 <- function(){ ... return() } main <-

Further to my email below, I have just realised that I forgot to include the specification of L and R. Hence, the code needs to include the following additional lines at the start;- L<-7.5e6 R<-2.5e6 Apologies for any confusion caused! Best regards, Tony > On 12 Jul 2017, at 10:03 AM, HUL-Anthony Egerton <aegerton at huntingtonunderwriting.com> wrote: > > I am trying

Tony, I?m not sure what exactly you?re trying to do, but you're not really taking advantage of vectorization in your R code. I've tried to clean it up a little. The clamped lognormal is almost always 0 or L? That seems a little odd. You seem to be using the inverse cdf method of drawing samples. That's not necessary in R for standard probability distributions. You may want to do a

Hi, I am completely new to GARCH models and trying to fit a multivariate time series model using DCC GARCH model and forecast it. The data looks like this: > head(datax) x vibration_x Speed 1 2017-05-16 17:53:00 -0.132 421.4189 2 2017-05-16 17:54:00 -0.296 1296.8882 3 2017-05-16 17:55:00 -0.572 0.0000 4 2017-05-16 17:56:00 -0.736 1254.2695 5