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Solar power and applied math

The applied math featured here tends to be fairly sophisticated, but there’s a lot you can do with the basics as we’ll see in the following interview with Trevor Dawson of Borrego Solar, a company specializing in grid-connected solar PV systems.

Trevor Dawson

JC: Can you say a little about yourself?

TD: I’m Trevor Dawson, I’m 25, born in the California Bay Area. I enjoy wood working, ceramics, soccer and travelling. I consider myself an environmentalist.

JC: What is your role at Borrego Solar?

TD: I am a Cost Reduction Analyst and I focus on applying Lean principles to identify and remove waste from both our internal processes and construction in the field. I use data to identify problems, prioritize them, and to verify the effectiveness of our solutions. I work with a variety of teams to reduce the cost or time of our projects.

Solar is a very fast-paced industry. Policy changes and technological improvements are being developed quickly and we have to respond quickly. A key function of my job is to assign measurable cost benefits to new practices and help ensure Borrego Solar continues to be an industry leader.

JC: What is your technical background and education?

TD: I graduated with a Bachelors of Science in Industrial & Systems Engineering (IE) from the University of Washington. I spent 3.5 years as an IE implementing process improvements on Boeing’s 777 Manufacturing Wing Line in Seattle, WA. I gained valuable experience in Lean, schedule optimization, design of experiments, and big data efforts. At Borrego, I get to apply those skills to help accelerate the adoption of the most time-tested, renewable energy source of all: the sun.

JC: What math, physics, or technical skills do you use every day?

TD: Addition, algebra, and simple statistics. I like to think I’ve mastered the basics. I also use a lot of my industrial engineering training to help gather and analyze data like design of experiments, time studies, and lean problem solving methodology.

I mostly work in Excel and use Power Pivot to drive large, cumbersome data into neat summary tables. Although the analysis can be a challenge, the hard work is rolling it up and presenting it in a way that is meaningful and convincing. When you’re suggesting a business decision, especially when it challenges the norm, your internal customers want to know the answer but they are equally interested in your process. For example, how does the business case change if a defined constraint or coefficient changes? The solar industry is dynamic and still maturing, so we have to be especially poised in our decision-making.

JC: What do you use much less than you expected?

TD: Calculus. I spent so much time learning calculus and even other things like differential equations but haven’t had much opportunity to apply them. However, I do think calculus taught me important practical problem solving skills and I put that to use now tackling large problems that span multiple pages.

JC: What math or technical skill do you wish you had more of or understood better?

TD: Excel programming and design. Excel rules the world, and although I was introduced to it at school, I think more intense courses should be commonplace. Regarding design, execution is the hardest part of any business decision, and design would help communicate results and suggestions much more effectively. A business needs verifiable proof that the suggested change is real and if executed will perform as predicted. This stage of verifying the effectiveness of a project could be improved with better design skills and may even reduce the amount of touch time and communications all the way through from inception to completion of a project.

JC: Anything else you’d like us to know?

TD: Go solar!

Amistics

Neal Stephenson coins a useful word Amistics in his novel Seveneves:

… it was a question of Amistics, which was a term that had been coined ages ago by a Moiran anthropologist to talk about the choices that different cultures made as to which technologies they would, and would not, make part of their lives. The word went all the way back to the Amish people … All cultures did this, frequently without being consciously aware that they had made collective choices.

Related post by Kevin Kelly: Amish Hackers

Retronyms and Backronyms

gear shift for a car with an automatic transmission

A retronym is a new name created for an old thing, often made necessary by technological changes. For example, we have terms like “postal mail” or “snail mail” for what used to simply be “mail” because email has become the default. What was once called a “transmission” is now called a “manual transmission” since most cars (at least in the US) now have an automatic transmission.

A backronym is sort of a fictional etymology, such as a meaning retrofitted to an acronym. Richard Campbell explains Structured Query Language is a backronym for SQL.

IBM’s first database was not relational. Its second database, DB2, was a sequel to its first database, and so they wanted to call its query language SEQUEL but they were unable to copyright the name. So they dropped the vowels, shortened it to SQL. Later someone came up with the backronym “Structured Query Language.”

The APGAR score for newborns is a mnemonic backronym. Virginia Apgar came up with her scoring system ten years before someone came up with the backronym Appearance, Pulse, Grimace, Activity and Respiration.

Flood control parks

flooded park

The park in the photo above flooded. And that’s a good thing. It’s designed to flood so that homes don’t.

It’s not really a park that flooded. It’s a flood control project that most of the time doubles as a park. Ordinarily the park has a lake, but a few days a year the park is a lake.

Harris County, Texas has an unusually large amount of public recreational land. One reason the county can afford this is that some of the recreational land serves two purposes.

Other Houston-area posts:

Kalman filters and bottom-up learning

radio antennae

Kalman filtering is a mixture of differential equations and statistics. Kalman filters are commonly used in tracking applications, such as tracking the location of a space probe or tracking the amount of charge left in a cell phone battery. Kalman filters provide a way to synthesize theoretical predictions and actual measurements, accounting for error in both.

Engineers naturally emphasize the differential equations and statisticians naturally emphasize the statistics. Both perspectives are valuable, but in my opinion/experience, the engineering perspective must come first.

From an engineering perspective, a Kalman filtering problem starts as a differential equation. In an ideal world, one would simply solve the differential equation and be done. But the experienced engineer realizes his or her differential equations don’t capture everything. (Unlike the engineer in this post.) Along the road to the equations at hand, there were approximations, terms left out, and various unknown unknowns.

The Kalman filter accounts for some level of uncertainty in the process dynamics and in the measurements taken. This uncertainty is modeled as randomness, but this doesn’t mean that there’s necessarily anything “random” going on. It simply acknowledges that random variables are an effective way of modeling miscellaneous effects that are unknown or too complicated to account for directly. (See Random is as random does.)

The statistical approach to Kalman filtering is to say that it is simply another estimation problem. You start from a probability model and apply Bayes’ theorem. That probability model has a term inside that happens to come from a differential equation in practice, but this is irrelevant to the statistics. The basic Kalman filter is a linear model with normal probability distributions, and this makes a closed-form solution for the posterior possible.

You’d be hard pressed to start from a statistical description of Kalman filtering, such as that given here, and have much appreciation for the motivating dynamics. Vital details have simply been abstracted away. As a client told me once when I tried to understand his problem starting from the top-down, “You’ll never get here from there.”

The statistical perspective is complementary. Some things are clear from the beginning with the statistical formulation that would take a long time to see from the engineering perspective. But while both perspectives are valuable, I believe it’s easier to start on the engineering end and work toward the statistics end rather than the other way around.

History supports this claim. The Kalman filter from the engineering perspective came first and its formulation in terms of Bayesian statistics came later. Except that’s not entirely true.

Rudolf Kálmán published his seminal paper in 1960 and four years later papers started to come out making the connection to Bayesian statistics. But while Kálmán and others were working in the US starting from the engineering end, Ruslan Stratonovich was working in Russia starting from the statistical end. Still, I believe it’s fair to say that most of the development and application of Kalman filters has proceeded from the engineering to the statistics rather than the other way around.

More on Kalman filters

 

Top tweets

I had a couple tweets this week that were fairly popular. The first was a pun on the musical Hamilton and the Hamiltonian from physics. The former is about Alexander Hamilton (1755–1804) and the latter is named after William Rowan Hamilton (1805–1865).

The second was a sort of snowclone, a variation on the line from the Bhagavad Gita that J. Robert Oppenheimer famously quoted in reference to the atomic bomb:

Spectral flatness

White noise has a flat power spectrum. So a reasonable way to measure how close a sound is to being pure noise is to measure how flat its spectrum is.

Spectral flatness is defined as the ratio of the geometric mean to the arithmetic mean of a power spectrum.

The arithmetic mean of a sequence of n items is what you usually think of as a mean or average: add up all the items and divide by n.

The geometric mean of a sequence of n items is the nth root of their product. You could calculate this by taking the arithmetic mean of the logarithms of the items, then taking the exponential of the result. What if some items are negative? Since the power spectrum is the squared absolute value of the FFT, it can’t be negative.

So why should the ratio of the geometric mean to the arithmetic mean measure flatness? And why pure tones have “unflat” power spectra?

If a power spectrum were perfectly flat, i.e. constant, then its arithmetic and geometric means would be equal, so their ratio would be 1. Could the ratio ever be more than 1? No, because the geometric mean is always less than or equal to the arithmetic mean, with equality happening only for constant sequences.

In the continuous realm, the Fourier transform of a sine wave is a pair of delta functions. In the discrete realm, the FFT will be large at two points and small everywhere else. Why should a concentrated function have a small flatness score? If one or two of the components are 1’s and the rest are zeros, the geometric mean is zero. So the ratio of geometric and arithmetic means is zero. If you replace the zero entries with some small ε and take the limit as ε goes to zero, you get 0 flatness as well.

Sometimes flatness is measured on a logarithmic scale, so instead of running from 0 to 1, it would run from -∞ to 0.

Let’s compute the flatness of some examples. We’ll take a mixture of a 440 Hz sine wave and Gaussian white noise with varying proportions, from pure sine wave to pure noise. Here’s what the flatness looks like as a function of the proportions.

spectral flatness

The curve is fairly smooth, though there’s some simulation noise at the right end. This is because we’re working with finite samples.

Here’s what a couple of these signals would sound like. First 30% noise and 70% sine wave:

(download)

And now the other way around, 70% noise and 30% sine wave:

(download)

Why does the flatness of white noise max out somewhere between 0.5 and 0.6 rather than 1? White noise only has a flat spectrum in expectation. The expected value of the power spectrum at every frequency is the same, but that won’t be true of any particular sample.

Related posts:

Linear or not, random or not, at different levels

Linear vs nonlinear

I’ve run across a lot of ambiguity lately regarding systems described as “nonlinear.” Systems typically have several layers, and are linear at one level and nonlinear at another, and authors are not always clear about which level they’re talking about.

For example, I recently ran across something called a “nonlinear trapezoid filter.” My first instinct was to parse this as

nonlinear (trapezoid filter)

though I didn’t know what that meant. On closer inspection, I think they meant

(nonlinear trapezoid) filter

which is a linear filter, formed by multiplying a spectrum by a “nonlinear trapezoid,” a function whose graph looks like a trapezoid except one of the sides is slightly curved.

One of the things that prompted this post was a discussion with a client about Kalman filters and particle filters. You can have linear methods for tracking nonlinear processes, nonlinear methods for tracking nonlinear processes, etc. You have to be careful in this context when you call something “linear” or not.

Random vs deterministic

There’s a similar ambiguity around whether a system is random or deterministic. Systems are often deterministic at one level and random at another. For example, a bandit design is a deterministic rule for conducting an experiment with random outcomes. Simulations can be even more confusing because there could be several alternating layers of randomness and determinism. People can talk past each other while thinking they’re being clear. They can say something about variance, for example, and other other person nods their head, though they’re thinking about the variance of two different things.

As simple as it sounds, you can often help a team out by asking them to be more explicit when they say something is linear or nonlinear, random or deterministic. In a sense this is nothing new: it helps to be explicit. But I’m saying a little more than that, suggesting a couple particular areas to watch out for, areas where it’s common for people to be vague when they think they’re being clear.

Related posts

Alphamagic squares in French

In earlier blog posts I give examples of alphamagic squares in English and in Spanish. This post looks at French.

French flag

The Wikipedia article on alphamagic squares, quoting The Universal Book of Mathematics, says

French allows just one 3 × 3 alphamagic square involving numbers up to 200, but a further 255 squares if the size of the entries is increased to 300.

My script did not find a French alphamagic with entries no larger than 200, but it did find 254 squares with entries no larger than 300. (For each of these squares there are 7 more variations you can form by rotation and reflection.)

The French alphamagic square with the smallest maximum entry that I found is the following.

[[3, 204, 102], [202, 103, 4], [104, 2, 203]]

When spelled out in French we get

[[trois, deux cent quatre, cent deux], [deux cent deux, cent trois, quatre], [cent quatre, deux, deux cent trois]]

And when we replace each cell with its number of letters we get the following magic square:

[[ 5, 14, 8], [12, 9, 6], [10, , 13]]

A list of all the French magic squares with maximum element 300 that I found is available here.

Acoustic roughness

When two pure tones are nearly in tune, you hear beats. The perceived pitch is the average of the two pitches, and you hear it fluctuate as many times per second as the difference in frequencies. For example, an A 438 and an A 442 together sound like an A 440 that beats four times per second. (Listen)

As the difference in pitches increases, the combined tone sounds rough and unpleasant. Here are sound files combining two pitches that differ by 16 Hz and 30 Hz.

16 Hz:

30 Hz:

The sound becomes more pleasant as the tones differ more in pitch. Here’s an example of pitches differing by 100 Hz. Now instead of hearing one rough tone, we hear two distinct tones in harmony. The two notes are at frequencies 440-50 Hz and 440+50 Hz, approximately the G and B above middle C.

100 Hz:

If we separate the tones even further, we hear one tone again. Here we separate the tones by 300 Hz. Now instead of hearing harmony, we hear only the lower tone, 440+150 Hz. The upper tone, 440+150 Hz, changes the quality of the lower tone but is barely perceived directly.

300 Hz:

We can make the previous example sound a little better by making the separation a little smaller, 293 Hz. Why? Because now the two tones are an octave apart rather than a little more than an octave. Now we hear the D above middle C.

293 Hz:

Update: Here’s a continuous version of the above examples. The separation of the two pitches at time t is 10t Hz.

Continuous:

Here’s Python code that produced the .wav files. (I’m using Python 3.5.1. There was a comment on an earlier post from someone having trouble using similar code from Python 2.7.)

from scipy.io.wavfile import write
from numpy import arange, pi, sin, int16, iinfo

N = 48000 # sampling rate per second
x = arange(3*N) # 3 seconds of audio

def beats(t, f1, f2):
    return sin(2*pi*f1*t) + sin(2*pi*f2*t)

def to_integer(signal):
    # Take samples in [-1, 1] and scale to 16-bit integers
    m = iinfo(int16).max
    M = max(abs(signal))
    return int16(signal*m/M)

def write_beat_file(center_freq, delta):
    f1 = center_freq - 0.5*delta
    f2 = center_freq + 0.5*delta    
    file_name = "beats_{}Hz_diff.wav".format(delta)
    write(file_name, N, to_integer(beats(x/N, f1, f2)))

write_beat_file(440, 4)
write_beat_file(440, 16)
write_beat_file(440, 30)
write_beat_file(440, 100)
write_beat_file(440, 293)

In my next post on roughness I get a little more quantitative, giving a power law for roughness of an amplitude modulated signal.

Related: Psychoacoustics consulting

Paying for doughnuts with a phone

At a doughnut shop today, I noticed the people at the head of each line were using their phones, either to pay for an order or to use a coupon. I thought how ridiculous it would sound if I were to go back twenty or thirty years and tell my mother about this.

Me: Some day people are going to pay for doughnuts with a phone.

Mom: You mean like calling up a doughnut shop to place an order? We already do that.

Me: No, they’re going to take their phone into the doughnut shop and pay with it.

Mom: Good grief. Why not just use cash?

Me: Well, they could. But it’ll be easy to use their phones since most people will carry them around all the time anyway.

Mom: People will carry around phones?!

Me: Sorta. More like computers, that can also make phone calls.

Mom: People will carry around computers?!!

old PC

Me: Not really. I was just making that up. But they will drive flying cars.

Mom: OK, I could see that. That’ll be nice.