August 2009 Archives

Thermographic surveys help guide contractors upgrading the thermal efficiencies of existing buildings. Source: Fluke Corporation 

The old saying is that "A picture is worth 1,000 words," but a picture plus quantitative measurements can be worth far more. A case in point: thermographic surveys of residential and commercial buildings.

Thermography is the science of remotely sensing the temperatures of objects by mapping their infrared emissions to provide a qualitative image showing hot- and cool-spots on surfaces visible in the image along with highly accurate quantitative temperature readings of selected spots.

Contractors can use this information to quickly find energy leaks in existing buildings and then determine the amount and type of insulation best suited to close them up. For example, it makes no sense to pack extra insulation into a home's attic, if most of the heating and cooling losses go through the walls. Similarly, adding more and more insulation in the walls would reach a point of diminishing returns if the windows became the dominant loss. Better to put just enough insulation into the walls and then concentrate on upgrading the windows.

Such surveys are appropriate to help planning remodeling projects and to verify energy efficiency of new construction projects. Just having thermal imaging equipment, however, does no good unless contractors know how to use and interpret it. Teaching those skills is the job of engineering technology programs at two- and four-year colleges.

Through the Fluke Weatherization Grant Program, instructors in accredited programs in building science, weatherization, energy conservation, home inspection and heating, ventilation and air conditioning (HVAC) can apply for grants of Fluke IR-InSIGHT thermal imagers to use in teaching. Fluke Corporation is donating the equipment to schools and training programs for use in teaching students to perform weatherization work and home inspections.

Instructors have just weeks to apply for $100,000 worth of infrared thermal imagers from Fluke Corporation. Twenty programs will be selected to receive one thermal imager kit including software, two rechargeable batteries, charger, operation manual and USB adapter. Complete guidelines and an application form are available at the Fluke Weatherization Solution Center. Deadline for applications is September 14, 2009. Fluke will announce the winners in September 2009.

For more information on the Fluke Weatherization Program, visit the Fluke Weatherization Solution Center, or contact Fluke Corporation, P.O. Box 9090, Everett, WA USA 98206, call (800) 44-FLUKE (800-443-5853), fax (425) 446-5116, or e-mail fluke-info@fluke.com.

The Mandelbrot set is the most famous illustration of a fractal in two dimensions. The same mathematics can be used in one dimension to describe financial market price activity. Source: Wikipedia

The other day I caught part of an interview with a spokesperson for Elliott Wave International on Bloomberg TV. Elliott Wave International is an organization devoted to promoting the use of a market-analysis technique developed in the 1930s by Ralph Nelson Elliott, called the wave principle. The most important thing to come out of the interview, to my mind, was an off-hand comment by the spokesperson that stock-price movement "is a fractal."

A fractal is a mathematical waveform with a texture that is of the same form at all spatial-frequency scales. For example, we've all seen those neat illustrations hung in hotel lobbies and meeting rooms that include what appear to be strikingly realistic silhouettes of mountain ranges. Artists make those illustrations by simply tearing a piece of construction paper, and airbrushing past the torn edge. Since construction-paper tears and mountain-range silhouettes are both fractals, the torn paper edge bears an uncanny resemblance to a generic mountain range.

The most famous fractal illustration, and the one mathematicians use most often to illustrate the properties of fractals, is the Mandelbrot set. I think people like to use it as an illustration because it's amazingly beautiful. It also has the advantage of clearly illustrating fractal properties. Unfortunately, it has no practical value, so using it to illustrate fractals leaves the impression that fractals have no practical value.

Nothing could be further from the truth.

Like all fractals, the Mandelbrot set repeats itself on all length scales. It thus displays the property of self similarity. Similarly, if you take a typical stock-price chart covering a time span of, say, one month, and compare it to an intraday chart, the two will look remarkably similar, and will also bear a striking resemblance to the mountain-range silhouette, and the torn paper. They're all fractals.

Self-similar charts, such as stock price charts, all have two spatial frequency properties: they're combinations of "tones" at a wide range of spatial frequencies, and the amplitude of any one tone is inversely proportional to its spatial frequency. Tones at lower spatial frequencies - so they take up more of the chart horizontally - also have proportionally higher amplitudes - so they take up more of the chart vertically.

Comparison of stock-price charts covering a wide range of time spans shows that they also show self similarity, and are therefore fractals. Financial markets exhibit this behavior because they are chaotic systems. That is, there are a large number of independent actors making buy/sell decisions based on different interpretations of the same enormous, but incomplete, data set. Each actor moves the price of an individual financial instrument slightly, but none has enough pricing power to exercise significant control alone. Everyone has an effect, but nobody has a controlling effect.

That is what drives fundamental stock analysts bonkers. Each property of the underlying company, each news item about the company, each pronouncement by a politician, has an affect - often a noticeable effect - but none of them has a determining effect. There's just too much else going on that also has an effect.

It is important to recognize that there are varying degrees of self-similarity. The Mandelbrot set displays the strongest type of self-similarity (exact self-similarity), meaning that it appears exactly the same at all spatial scales. Stock prices, on the other hand, display the weakest type of self-similarity (statistical self-similarity), meaning that the figure has numerical or statistical measures that are preserved over different spatial scales. The fact that stock charts have only statistical self-similarity is what makes stock-price movements inherently unpredictable.

Because Elliott wave analysis starts from the observation that charts of financial-market prices are fractals, the wave principle, out of all technical analysis tools, has the best hope of making sense of financial-market price movements. Elliott's original work was hampered by the fact that understanding of fractals was limited in the 1930s, and understanding of chaotic systems was effectively nil until the 1960s. Some misunderstandings, such as the representation of waves as triangular, rather than sinusoidal, still persist, but anyone interested in learning technical analysis could do a lot worse than beginning with Elliott wave analysis.

Increased funding for science projects flows into the general economy through expanding needs for support facilities. Here construction workers begin blasting for a neutrino detector facility in Minnesota to capture neutrinos generated by a Fermilab accelerator in Illinois. The project is being made possible by American Recovery and Reinvestment Act funds. Source: Fermilab

To most people, the American Recovery and Reinvestment Act is about creating jobs. So, why should it include more than $327 million in new funding announced early this month go toward scientific research, instrumentation, and laboratory infrastructure projects?

The answer is that job creation, while the primary concern, is not the only consideration the Obama Administration has when deciding where to put our tax dollars. If possible, they like to see projects that provide long-lasting benefits that keep on giving long after the jobs are created.

In addition to immediate job creation, dollars spent on scientific research stimulate advances in the technology our society depends on, and generate business for high technology companies. That's a trifecta that few infrastructure projects - and no make-work projects - can equal.

An example is the approximately $60 million provided to Fermilab in Batavia, IL that, combined with over $40 million provided earlier this year, is providing dividends in all three areas.

On the jobs front, science projects funded by the Act require expansions of facilities built by construction workers, electricians, and all the other trades needed to put up new buildings. Of course, jobs are also created for the scientists, engineers, and support people who do the science. And, don't forget the jobs for teachers, policemen, grocery store clerks, bank managers, and everyone else in the local communities where those scientists, engineers, and support people - and their families - work and live. One of the things they taught us in MBA school was that for every job you create directly, several additional jobs are created indirectly.

In addition, advanced-science projects generally require developments of new technology along the way. For example, research at Fermilab aimed at making more advanced particle accelerators, also funded by the Act, is developing new superconducting materials that can be used in a wide range of applications from medical imaging to more efficient electricity distribution.

Finally, researchers developing those magnets will purchase the bismuth-based material from US vendors to conduct cabling and coil studies, and will partner with businesses to encourage industrial fabrication of high-field magnets, an effort that could result in cutting edge technologies for other applications.

Similar results accrue from other science projects being funded by the American Recovery and Reinvestment Act. While some observers may question the value of earmarking tens of millions of dollars of recovery funds to make jobs for a few scientists, better informed people recognize that spending on scientific research provides big tangible returns even before gaining the intangible returns of expanding our understanding of the Universe.

Volcano monitoring is a task that the Three Ds say definitely should be automated. Source: NASA

I've had occasion to write articles about factory automation several times, and one question that often comes up is: "Why automate a manual process?" In the short run, automation is expensive. It's a lot cheaper to keep running the same old manual system (especially if it's working well) than to take on the capital expense of replacing it with automation.

Any automated system replacing a manual one will be, by definition, novel. There is large technical risk in any novel system. Experienced engineers know that nobody is smart enough get it right the first time (at least not with any consistency). There are always things you don't know, forgot, or did just a little bit wrong - not to mention the dreaded unintended consequences that plague any complex system.

These days, it's possible to automate virtually any task. The challenge in the industrial engineering field is to interlink islands of automation into what my friends at Siemens like to call "Totally Integrated Automation" (TIA).

There are, however, still a few tasks that are manual in nature. Folding them in under the TIA umbrella, whether using technology from Siemens or another factory automation equipment vendor, as manual systems is problematic. There is a tendency to automate any task as a knee-jerk reaction to manualism.

That can be a mistake. Not everything should be automated, even in a TIA environment. Some things people are better at doing than machines. There aren't many, and the number grows fewer as automated systems become ever more capable. But, they are still there, and represent big land mines for system integrators.

The issue will also start to impact consumers in the general public as embedded control systems spread throughout society. In fact, it's already becoming significant in the automotive space, as systems become commercialized to monitor (and correct) driver actions that the computers deem suspect. Poor shifting habits were the first to succumb to the engineers' heavy hands with automatic transmissions. Then, decades later, overbraking by panicked drivers was theoretically eliminated by anti-lock brake systems (ABS). Now, we're poised for a host of computer intrusions into the driving process, from falling asleep at the wheel to clumsy parking techniques.

There are a number of criteria that can be used to decide when to automate a task, but the earliest, and still the most universally applicable, is the Three Ds. The Three Ds hail from the early days of robotics, when doing anything automatically was a major challenge. It's a razor that can be used to divide sharply between what is essentially for humans to do, and what is fair game for automation.

(A razor is a logical device used to guide difficult this versus that decisions. The famous Occam's Razor, which tells you to always favor the simplest hypothesis that explains the facts, is a well known example. Razors should be short, easy to understand and apply, and unambiguous. It also helps if the actually work!)

The Three Ds are "dirty, dull, and dangerous." The razor says that any task that exhibits even one of these characteristics should be considered for automation. If it exhibits any two, its a strong candidate for automation with all deliberate speed. If it exhibits all three, get the humans out of there as fast as their little legs can carry them.

Recently, NASA deployed some robotic sensing devices atop Mt. St. Helens that demonstrate how to apply the Three Ds. The task is to carefully monitor a number of significant variables at hot spots on the volcano.

Dirty does not just mean a tendency to get coated with unspecified unpleasant guck. I once had a summer job cleaning the hard-water scale from the insides of boiler tubes. It came out as nano-scale red powder particles suspended in the air. That was a traditionally dirty job. It was also dirty in a wider Three Ds sense: ambient conditions were such as to physically stress human organisms. Basically, the insides of boilers were uncomfortably hot. Not quite hyperthermia-inducing hot, but hot enough that you didn't want to be in there any longer than you had to be. While being outdoors on the top of a high mountain might seem an ideal environment to a city dweller locked in an office, to those of us who've been left out in the elements long enough to feel the effects of exposure, it qualifies as mildly dirty. Add in noxious vapors and other things that tend to leak out of volcanic hot spots, and it gets dirty, indeed.

Dull really means tedious. Anything repetitive, especially if the situation requires constant attention, is dull. Again, data logging is something that sounds like a walk in the park to those who haven't done it manually. I remember one day as an undergraduate student, when I was studying the stability of an oscillator I'd just finished building. I set the thing up with a frequency counter displaying measurements to six digit accuracy on a nixie-tube display. This was before the days of LED readouts, and long before PC-based data acquisition. Only the last two digits were changing. I sat in a (happily reasonably comfortable) chair writing down the last three digits every 30 seconds for six hours straight. No bathroom breaks. No talking with the guy at the next bench. No reading a book. That taught me the real meaning of dull. The poor robots on Mt. St. Helens are tasked with doing that job 24/7 with the only reprieve coming when the mountain next blows its top and ends their miserable existences.

Dangerous means who or what is undertaking the task is in imminent danger of annihilation, or at least grievous bodily harm. NASA's robots weren't put in nice, safe locations. They were put in places the volcanologists deemed most likely to vaporize catastrophically, taking the robots' spindly little bodies with them.

Folks - and you're going to see a lot of them in the next year or so as the economic recovery seems endlessly "jobless" - who complain that automation is taking away their jobs should heed the Three Ds. The only people that automation (properly done) will put out of work are those who are so stupid they embrace tedium, so expendable they get sent into the lion's maw, or so desperate that they're willing to work under inhuman conditions. The rest of us will make do with the good jobs.

Figure 1: Chevy plans to introduce an electric car called the "Volt" claimed to get 230 mpg.

Chevrolet leads off the content at its website for the Volt electric car with a cryptic explanation of the test conditions that allow them to claim 230 mpg fuel economy for an electric car. That's good, because nobody in their right mind would accept such an outlandish figure without at least a stab at knowing the test conditions. It's bad because fuel economy figures for an electric vehicle are meaningless, since an electric vehicle does not run on fuel.

Hybrid automotive propulsion systems run primarily on fuel (gasoline, diesel, liquified natural gas, etc.) with a means of capturing energy generated when the car's propulsion demand is lower than the engine's available output, then delivering it back when propulsion demand is high. That allows the vehicle to have an undersized engine and still deliver bursts of performance equivalent to a car with a much more powerful engine.

As an example of how this works in practice, some Formula One race cars use a similar technology called the kinetic energy recovery system (KERS). While braking for a corner, the KERS system captures some of the energy that would be burned off as heat in the brakes and stores it in a battery. The driver has a push button that pours that energy back through the drive train, delivering some 80 HP in excess of the engine's maximum output.

Two anecdotes are available from this experiment. First, it is said that KERS equipped cars are nearly impossible for non-KERS cars to pass under acceleration. Does that surprise anyone? The second bit of information is that the leader in this year's F1 constructors championship does not use KERS. What that really means, and what the standings will be at the end of the season are valid topics for beer hall debates.

Electric vehicles - the Volt included - are primarily driven by electric motors. From an engineering standpoint, there's a lot to be said for this architecture. It's well understood. It vastly simplifies the mechanical drive train. Leaving out the battery pack, it reduces the powerplant weight by a lot. Consequently, it will likely reduce the energy cost of getting the payload from A to B. It probably will reduce maintenance requirements as well, since the components are few, quite robust, and don't suffer much wear.

I said that the technology is well understood. It is and has been for a very long time. Some of the earliest experiments in automotive technology were electric vehicles. I drove an electric forklift during the 1960s. My grandson has owned and operated a series of electric go karts for most of his life. Electric golf carts dot the fairways of America. Electric vehicle technology has been under development for at least as long as the internal combustion engine. What we know about it is a lot!

The problem, which I sidestepped above, is that battery technology is such that storing the energy needed to get that payload from A to B is enormously bulky and heavy. If A and B are significantly far apart, the size and weight of the battery becomes impractical.

The problem is storing enough energy in electric form to run the thing a reasonable distance. The Volt gets around this problem by installing an auxiliary power unit (APU) to provide power when the driver has been foolish enough to drive past the vehicle's point of no return on battery power, and wants to get back by, say, 3:45 PM for a meeting. The APU kicks in, providing enough juice to get you home.

Altogether, I have no problem with the Volt itself. It looks great - or at least as good as allowed by the inaesthetic drech that passes for automobile styling today. Not having driven one myself, yet, I can't answer for its performance, but electric vehicles generally can be made to go as fast as you want (for a while). From my comments above, you can tell that I like the concept from an engineering standpoint - especially for short trips with long recharge spells in between.

My problem is with the idea of assigning a miles per gallon performance figure. It makes sense for hybrid vehicles because the whole driving experience is energized by fuel from a tank. For an electric vehicle, with the bulk of the energy theoretically supplied by an electric outlet, it is rediculous.

So what if the current test protocol returns a result of 230 mpg? I can double that just by jiggering the test protocol for shorter trips so the horse gets back to the barn for more oats before having to limp the last few furlongs. If the car is good for 40 mi on battery power, I can jack the fuel economy rating to infinity just by allowing test trips of 39.5 mi.

It's just soooo bogus!

Figure 1: Financial markets exhibit variations at all time scales with amplitude proportional to period. Based on data from stockcharts.com.

Yesterday kicked off the first day of the NIWeek 2009. This is the annual National Instruments user group meeting. NI to followers of this company, makes hardware and software for data acquisition and control applications. For general engineering and research laboratory software, the company's LabVIEW product enjoys a dominant position. The way the company has built and maintains this position is by working hard to anticipate major technology trends that will affect their users, and what their executives learn from this ongoing exercise comes out in comments during keynote speeches at NIWeek.

During NI Chairman James Truchard's opening comments this year, he expressed the opinion that some of the economic difficulties we have experienced globally over the past year can be traced to inappropriate use of mathematics in finance

"... Mathematics, the way it has been taught in business schools the last few years," he said, "has a serious problem ..."

Dr T, as he is known to his friends in the industry, also trotted out the oft-quoted comment by Warren Buffet: "It seems like the higher mathematics, with more false precision, should help you, but it doesn't."

This reminded me of a couple of themes I've been studying over the past few years chaos and fuzzy logic as they apply to things in general, and financial markets in particular. These are surprisingly easy to understand, easy to explain, and universal in application.

Chaos has bad connotations in western philosophy, which guides most of what we do for better or worse. In western thought, chaos is the enemy of order, and one of western civilization's stated goals is maintaining order. Eastern philosophy, on the other hand, seeks to balance the "forces" of yin and yang. These are complex concepts, but the important point for this discussion is that, among other things, yin is an order-seeking force, and yang is a chaos-seeking force. The theory is that both yin and yang are necessary for good health, and specifically must be maintained in an dynamic balance.

Mathematically, chaotic systems can most easily recognized by being in constant motion with cycles represented at all time scales, and the amplitudes of these motions are roughly proportional to their periods. Thus, weekly fluctuations tend to have an amplitude 5-10 times larger than daily fluctuations, with the largest amplitude movements appearing on decades-long time scales.

What makes chaos important is that it can be shown to govern the operation of virtually every complex system, from ocean waves to all human history. If it's a large dynamic system with lots of independently moving parts, its behavior most likely is chaotic.

Fuzzy logic, on the other hand, seems unmathematical for a different reason. The term sounds like a synonym for "fuzzy thinking," which people associate with foolishness and ignorance. Mathematically, however, fuzzy logic is a way of dealing logically with things that cannot be precisely quantified.

Humans, in fact, make nearly all personal decisions - especially life-and-death decisions - using fuzzy logic.

When you merge your car into a busy traffic lane, for example, you don't get out a radar gun to precisely measure the speeds of vehicles already in the lane, then analyze video images of the spacings between cars, then do a calculation based on the measured data plus known performance of your vehicle at various settings of throttle and steering controls. You don't have the equipment available to make the measurements, and you wouldn't have time available to make the calculations if you did.

Instead, your brain converts sense impressions and memories from previous encounters into more-or-less broad categories. Spaces between other cars, for example, are "tight," "okay," or "really big." Their speeds are "slow," "medium," or "fast." Your car "accelerates fast," "moves out okay," or "handles like a dead whale." Instead of a dozens of variables with 12-bit precision, your brain reduces the data set to a handful of variables with at most two-bit precision. It also doesn't use arithmetic to work out the answer, but a fuzzy logic algorithm that resembles a decision tree. The answer comes back as "it's safe - go for it;" "gee, it's borderline," or "that looks like a trip to the emergency ward." The final fuzzy calculation says "no" in the last instance, causes you to look further in the middle instance, and spurs you into action in the first instance.

That's fuzzy logic. It's the fastest and most reliable way to make difficult decisions when time is limited (as it always is). It's also virtually the only way to deal with chaotic systems, which, as I said above, almost all real systems are.

Probably (in a fuzzy-logic sense) the way to solve the problem Truchard and Buffet identified, is by mentally taking chaos and fuzzy logic out of the "advanced mathematics" cubbyhole, and teach it to high-school freshman. If we did that, we'd quickly have a generation with the mental tools needed to analyze and deal with the real world, rather than a hypothetical world of mathematical abstractions.