Electromagnetic Propulsion of Ships and Submarines

The other day, I was watching The Hunt For Red October on TV. Through some odd coincidence, today I found a link to an article that was published in Popular Science back in 1966 on a silent electromagnetic drive for submarines, just like the “caterpillar drive” of the Red October. I didn’t realize that this was a real thing! It seems like it would be a fun experiment to build an demonstration showing this effect, and indeed, this page links to some articles that would seem to be helpful.

Electromagnetic Propulsion Ships, Submarines: patents & articles

Bookmarked for later perusal.

“MOAR POWER!” … Wait… I mean, “LESS POWER!”

My electronics experimentation has brought a couple of comments from people I’ve met who have much greater experience and knowledge than I. For instance, in response to my posting of a schematic for flashing a rather large, powerful LED yesterday, I drew the attention of Mike Colishaw on Twitter:

@ Hmm, wasting half the power in resistor 🙁 See: http://t.co/IqyX8x0P (schematic at http://t.co/HekNVW5j)

@MikeCowlishaw

Mike Cowlishaw

And of course, Mike is absolutely 100% correct. The circuit that I posted is inexcusably inefficient. But of course, I have an excuse (a couple in fact).

First of all, there is the relatively lame “everybody does it.” If you go to section 7.3 of the Arduino Cookbook, you’ll see this exact circuit, with nary a mention of the relatively silly amounts of power wasted. I’d submit that in many battery powered applications, the power wasted by driving even ordinary LEDs this way can be quite large, and techniques to drive them more efficiently should be better known. Mike’s circuit is for a spelunker’s light, so battery life is obviously very, very important, and yet the overall circuit is pretty simple. Worthy of your (and my) study. When I get back to working on my light based communication experiments, I’ll want to make sure I’m driving these powerful LEDs efficiently, so I’ll be studying this stuff a lot harder.

My second excuse is one of simple expediency: Halloween is just a few days away, and I wanted to get this working. Doing a better job would require more parts, and probably parts I don’t have in my junk bin.

I’ll unveil the (very simple) project that this tinkering is a part of shortly. Stay tuned.

Addendum: I just learned about the Twitter Blackbird Pie plugin which allows you to embed tweets into WordPress plugins.   Very nice little gadget, and I’ll be using it more in the future.

Approaching 100K views this year… Thanks!

This morning, I consulted the little revolving map over there in the left column, and found that I had passed 99,000 views. I reset that counter back in February, and since then nearly 100K people (or more realistically, 99K robots and maybe 1K people) have viewed a page here on brainwagon. I’d like to thank all of the people who take time to read my chaotic jumble, and would especially like to thank those who take the time to comment. While this blog is mostly a sort of personal journal, your participation makes the time I spend here seem more valuable.

I hope in the next year to continue to improve and expand the kind of materials that I post here, documenting more of my own projects, and linking with more people who share a common philosophy. I hope you will stay tuned and participate going forward.

And thanks again to you all!

Nifty paper on Batting Average…

For some reason, I never do much reading about baseball during the season itself. But as the World Series approaches its end (still hoping for a game seven) I have started to dust off some of my reading materials. A couple years ago, I mentioned this work by Lawrence Brown on this blog, but the paper that he was writing was still a work-in-progress. But it’s available now:

IN-SEASON PREDICTION OF BATTING AVERAGES: A FIELD TEST OF EMPIRICAL BAYES AND BAYES METHODOLOGIES
By Lawrence D. Brown, University of Pennsylvania
.

Batting average is one of the principle performance measures for an individual baseball player. It is natural to statistically model this as a binomial-variable proportion, with a given (observed) number
of qualifying attempts (called “at-bats”), an observed number of successes (“hits”) distributed according to the binomial distribution, and with a true (but unknown) value of pi that represents the player’s latent ability. This is a common data structure in many statistical applications; and so the methodological study here has implications for such a range of applications.

There is a lot of meat to chew in it, but some easy take aways are that simply imagining that the batting average of a player in the first half of the season is indicative of their performance in the last half is just not true: it turns out to be a relatively poor measure. A better measure is simply to average that performance with the mean batting average of all players (best predictor is that the player will regress towards the mean). I’ve been aware of this principle for quite some time, but Brown goes on to derive some more interesting statistical techniques, well outside my normal comfort zone. If math/statistics is your thing, check it out.