Archive for October 2008
Article #51; (August 23, 2006)
“Basic Engineering and Physics Problems Transformed Mathematically” (draft)
This article exposes several practical exercises in engineering and physics that experimental data or observations had generated laws. Consequently, the resolution of their mathematical equations provided the necessary incentives to open up new mathematical methods and fields of studies.
Conversely, many mathematical discoveries done on purely theoretical basis lead to practical applications later on. We should keep in mind that mathematical equations are merely an abstraction of the reality, obtained by disregarding certain physical facts which seem to be of minor influence. In complicated physical situations, there may be no way of judging a priori the importance of various circumstances.
Let us state that rate of a quantity is the proportion of change in that quantity over a specific duration; the mathematical definition is written as the differential of that quantity (Q) over time (t) or dQ/dt. For the following exercises the first step of the mathematical formulations of the laws will be provided without any attempt to solving them:
1) Newton’s law of cooling: Experiments show that the rate of change of the temperature T1 of a conductive material to heat is proportional to the difference between T and the temperature of the surrounding medium. The mathematical formulation is: dT/dt = -k*(T1-T2); where k is a positive constant. Let us suppose that a copper ball is heated to a temperature T1 = 100 degrees C. At time t=0 the ball is placed in water which is maintained at a temperature T2 = 30. At the end of 3 minutes the temperature of the ball is reduced to T3 = 70. Find the time at which the temperature of the ball is reduced to T4 = 31.
2) Newton’s law of gravitation: Experiments show that the acceleration (a) of a body is proportional to inverse of the square of the distance (r) between the body and the center of the earth, or a(r) = k/r(2). Find the minimum initial velocity of a body which is fired in radial direction from the earth and is supposed to escape from earth; neglect the air resistance and the gravitational pull of other celestial bodies. Formulation: a(r) = – g*R(2)/r(2)
3) Torricelli’s law: Experiments show that the velocity with which a liquid issues from an orifice is v = [0.6*square root (2*g*h)] where h is the instantaneous height of the liquid above the orifice and the constant 0.6 was introduced by Borda to account for the fact that the cross-section of the out flowing stream of liquid is somewhat smaller than that of the orifice. A funnel whose angle at the outlet is 60 degrees and whose outlet has a cross-sectional area of 0.5 cm(2), contains water at a height of h = 10 cm. At time t = 0 the outlet is opened and the water flows out. Determine the time when the funnel will be empty. Formulation: the change in volume of water lowing out during a short interval of time is: dV = 0.5* v*dt.
4) Boyle-Mariotte’s law for ideal gases: Experiments show that for a gas at low pressure (p) and constant temperature (T) the rate of change of the volume is: dV = -V/p
5) The radiation of element radium law: Experiment show that radium disintegrates at a rate proportional to the amount of radium (M) instantaneously present or: dM/dt = k*M. What is its half-life or the time in which 50% of the amount M will disappear? If the half-time is 1590 years, then what per cent will disappear in one year?
6) The atmospheric pressure law: Observations show that the rate of change of the atmospheric pressure (p) with altitude (h) is proportional to the pressure, or dp/dh = – k*p. If p at 18,000 ft is half its value at sea level, find the formula for the pressure at any height.
7) The evaporation law: Observations show that a wet porous substance in the open air loses its moisture at a rate proportional to the moisture content (Q) or: dQ/dt = -k*Q. If a sheet hung in the wind loses half its moisture during the first hour, when will it have lost 99% under the same weather condition?
8) Sugar cane dilution law: Experiments show that the rate of change of the inversion of cane sugar in diluted solution is proportional to the concentration (Y) of the unaltered sugar, or d(1/Y)/dt = k*Y. If the concentration is 1/100 at t = 0 and is 1/250 at t = 5 hours, then find Y(t).
9) The law of boiling liquids: Observations show that the ratio of the quantities of two liquids of each passing off as vapor at any instant is proportional to the ratio of the quantities x and y still in the liquid state, or dy/dx = k*y/x.
10) Lambert’s law of absorption: Observations show that the absorption of light in a very thin transparent layer is proportional to the thickness (x) of the layer and to the amount incident (A) on that layer, or dA/dx = -k*A.
11) The law of mass action: Experiments show that the velocity (v) of a chemical reaction, under a constant temperature, is proportional to the product of the concentrations (a) and (b) in moles per liter of the substances which are reacting. If y is the number of moles per liter which have reacted after time (t), then the rate of reaction is: dy/dt = k*(a – y)*(b – y).
12) Falling body law: Experiments show that, if a body falls in vacuum due to the action of gravity and starting at time t = 0 with initial velocity v = 0, then the velocity of the body is proportional to the time or ds/dt = k*t, where s is the displacement or the distance of the body from its initial position.
Article #50, (September 10, 2006)
Computational Rationality in Artificial Intelligence
The field of Artificial Intelligence would have been more on target if it was called “Computational Rationality”, which is fundamentally the approach of the fourth system: Any scientific field, to be considered mature, a consensus by the professionals has to be reached; and the roots of the field of knowledge should be firmly grounded on mathematics and recognized traditional “rational logic”.
In order to have a holistic view of the elements that are considered in AI, it would be interesting to picture a table of basic elements such as designed for the chemistry elements. Thus A for atom, Ab for alpha-beta search, Al for algorithm, Af for automatic theory formation, At for augmented transition net, B for Bayes’ rule, Bf for breadth first search, Bl for blocks-world labelling heuristics, Br for binary relations, Bt for backtracking search, C for concept formation, Cd for COND, Cf for closure of functions, Cg for context-free grammar, Cl for cellular logic, Cn for constraint, Cr for circular reaction paradigm, Cs for CONS, Df for differentiation, D for depth, Dc for Dempster-Shafer calculus, De for debugging, Df for DEFUN, Di for discovery, Dm for dialogue management, Dn for discrimination network, Ds for depth first search, E for expertise, F for frame, Fc for forward chaining, Fm for formula manipulation, Fz for fuzzy logic, H for heuristic, Hm for the Hough transform, Ht for Herbrand’s theorem, I for ISA hierarchy, In for integration, Ie for inference engine, If for image formation and acquisition, Ih for inheritance, In for inference network, Ir for image representation, Kr for knowledge representation, L for lexicon, Lp for LISP, Lr for logical reasoning, Ls for LEIBNIZ structure, M for morphology, Mp for MAPCAR, Nm for numerical model, P for Prolog, Pa for pattern matching, Pb for probability, Pc for propositional calculus, Pd for predicate calculus, Pf for preprocessing of low-pass filtering, Pg for pragmatics, Pl for planning, Pp for parallel processing, Ps for problem solving, Pr for production rule, Py for PYTHAGORUS, Q for quantization, R for robotics, Ra for Ramer’s algorithm, Rb for rule-based systems, Rc for recursive lists, Rd for relational database, Re for relaxation, Rn for resolution, S for semantics, Sc for schemata, Sf for sufficiency factor, Sg for segmentation, Sh for shell, Sn for semantic net, Sp for script, Sq for SETQ, SS for state space, St for stereo, Sx for S-expression, Sy for syntax, U for unification, T for topology, Tt for Turing’s test, V for version space, Z for zero-sum games.
It would be interesting to classify these AI elements into chapters such as: Programming in LISP, Productions and matching, Knowledge representation, Search methods, Logical reasoning, Probabilistic reasoning, Learning, Natural-language understanding, Vision, and Expert systems…
Is our intelligence that natural or acquired through customs, tradition, formal teaching, standards of moral value, community consensus…Even a person living in a forest, away from any mankind habitation and communication, he is learning from animal behaviors communicated from customs, tradition, teaching processes inculcated by the particular animal communities…
How mankind intelligence differ and developed? Mostly through trading with other human communities: Exchanging expert tools of production, learning mechanisms, various customs and traditions…Variety of perspectives to looking at life, the universe and survival processes…
(To be developed further)
New semester, new approach
Posted on: October 26, 2008
Article #42 (April 6, 2006)
“New semester, new approach to teaching the HF course”
This semester ten students enrolled for my class; only one is a computer engineer finishing his degree and the remaining are industrial engineers. As a reminder, this course is required for IE and the other engineering disciplines managed to open up new elective courses and were trying to market them at the expense of the wishes of many students who wanted to take my course and their petitions were denied.
With a class, one fourth its usual number, I had to capitalize on the advantages of smaller classes, once the shock is under control. This semester, methods applied in human factors engineering are the focus and the reduction to half the previous semester of body of knowledge in the course materials might encourage my class to appreciate the efforts and time invested by the pool of human factors researchers and professionals to make available practical design guidelines for the other engineering professions.
Whereas in the previous semesters I shun away from exposing my class to new methods, except teaching them explicitly the concept of controlled experimentations, the differences among dependent, independent and controlled variables and correcting their misunderstanding, thinking that there was an abundance of knowledge to assimilate for a meager semester, I boldly changed direction in my teaching approach by investing more time on exposing and explaining the various methods that human factors might be applying in their profession. The first assignment was using excel to compare 40 methods used in human factors, industrial engineering, industrial psychology, and designers of intelligent machines. This assignment was a version of article #14, about the taxonomy of methods, from 20 articles that I wrote the previous years and offered them as an introduction to the course, in addition to the course materials. The students were supposed to select five categories from more than the dozen ways to classifying methods such as definition, purpose, applications, inputs, processes, procedures, output/product, mathematical requirements, disciplines teaching them, advantages, disadvantages, sources/links, connections with other methods, and comments.
I expected that, as engineers, they would logically select for columns applications, input, procedure, output, and comments because they are what define a method but somehow they opted for applications, procedures, advantages, disadvantages, and comments mainly because it is how the internet offer information. After 3 students submitted their assignment on time I handed them over 40 summary sheets for the 16 methods used to analyzing a system or a mission, at least 2 sheets for each of 16 methods, one sheet on the purpose, input, procedure, and output/product of the method and the other sheets as examples of what the output is expected to look for presentation. I then asked the less performing students to concentrate on only the 16 methods for their assignment and most of them did not submit this assignment even two months later.
So far I used up six sessions for methods or related topics such as the methods applied in the process of analyzing systems’ performance, psychophysical procedures, the fundamentals of controlled experimentation methods, human factors performance criteria, and what human factors measure in their experiments.
As for the body of knowledge I extract a few facts from experiments and asked them to participate in providing me with the rationales or processes that might explain these facts. For example, if data show that females on average are two third the strength of males then what could be the underlying causes for that discovery? Could that fact be explained by the length of the muscles, the cross section thickness of the muscles, the number of muscle fibers, or the length of the corresponding bones?
Facts are entertaining but I figured that they are big boys to be constantly entertained while shovelful of money is being spent for their university education. Facts are entertaining but there have to come a time when these big boys stop and wonder at the brain power, Herculean patience, and hard work behind these amusing sessions.
The next assignment was to observe the business of the family’s bread earner, note down the minute tasks of his typical day work, learn about the business by attempting to generate detailed answers from a questionnaire they have to develop based on a set of investigative query and problems related to human factors performance criteria in the assignment sheet, and to report back what are the routine and daily tasks that enabled the students to join a university. Three students worked with their fathers’ in summer times and enjoyed the assignment; the remaining students could not shake off their 8th grade habits, wrote the questionnaire, mailed it, and waited for the answers. I was expecting that the students would apply the methodology they learned in analyzing systems such as activity, decision, and task analyses but the good stuff was not forthcoming. To encourage them to cater to the business that they might inherit, I assigned them a lecture project that would generate the requisite analyses with a clear objective of focusing on near-accidents, foreseeable errors, safety of the workers and heath conditions in the work place.
So far, the products of the two quizzes were complete failures; although most of the questions in the second quiz were from the same chapter sources as the first quiz, it is amazing how ill prepared are the students for assimilating or focusing on the essential ideas, concepts, and methods. So far, with a third of the semester over, I can points to only two students who are delivering serious investment in time, hard work, and excitement and are shooting for a deserved grade of A.
Article #35 (Started March 4, 2006)
“Efficiency of the human body structure”
This article is an on going project to summarize a few capabilities and limitations of man. While the most sophisticated intelligent machines invented by man may contain up to ten thousand elements, the human machine is constituted of up to a million trillion of cells, up to a thousand trillions of neurons in the central nervous system, about a couple hundred bones, and as many organs, muscles, tendons and ligaments.
In the previous article #33 we discussed a graph in a story style and discovered that a human barefoot in texture, shape, and toes has a higher coefficient of friction than many man-made shoes that allow easier traction to move forward for less energy expenditure. We also expanded our story to observe that the structure of the bones and major muscles attached to limbs for movements as lever systems provide higher speed and range of movements at the expense of exorbitant muscular efforts.
A most important knowledge for designing interfaces is a thorough recognition of the capabilities and limitations of the five senses. One of the assignment involves comparing the various senses within two dozens categories such as: anatomy, physiology, receptor organs, stimulus, sources of energy, wave forms, reaction time, detectable wavelengths and frequencies, practical detection thresholds of signals, muscles, physical pressure, infections and inflammations, disorders and dysfunctions, assessment, diagnostic procedures, corrective measures, effects of age, and safety and risk.
Human dynamic efforts for doing mechanical work is at best 30% efficient because most of the efforts are converted to maintaining static positions in order to preserve stability and equilibrium for all the other concomitant stabilizing joints, bones and muscles. For example, the stooping position consumes 60% of the efforts for having a work done, in addition to the extremely high moment effected on the edges of the lower back intervertebrae discs. Static postures constrict the blood vessels and fresh blood is no longer carrying the necessary nutrients to sustain any effort for long duration and heart rate increases dramatically; lactic acid accumulates in the cells and fatigue ensues until the body rests in order to break down that acid.
Human energy efficiency is even worse because most of the energy expended is converted into heat. Not only physical exercises generate heat but, except for glucose or sugar, most of the nutrients have to undergo chemical transformations to break down the compounds into useful and ready sources of energy, thus generating more heat. Consequently, heat is produced even when sleeping when the body cells are regenerated. Internal heat could be a blessing in cold environments but a worst case scenario in a hot atmosphere because the cooling mechanism in human is solely confined to sweating off the heat accumulated in the blood stream. Heat is a source of blessing when we are sick with microbes and bacteria because the latter is killed when the internal body temperature rises above normal.
“How to tell long and good stories from human factors graphs?”
Article #33, (Feb. 28, 2006)
If we concentrate on a graph we might generate a long story that span many disciplines and furnish us with a wealth of information and knowledge that pages of words barely can convey. A graph might open the gate for dozen of questions that are the foundation of scientific, experimental, and critical thinking.
Suppose that we are comparing the efficiency in energy consumption between walking bare feet or wearing shoes that weight 1.3 Kg. Considering the walking speed as the other independent variable, along with the type and weight of shoes, we observe that the curves show that we are consuming less energy at low speed, then both curves decreasing to a minimum consumption of 0.2 KJ/Nm and intersecting at around 80 meter/min and then increasing as walking speed increases.
This graph is telling us that casual walking consumes less energy per unit walking effort than fast walking and that, at a cut off speed of 80 meter/min, the energy consumption is equal for both foot wares. Some people might jump to the conclusion that this cut-off speed can be generalized to all foot wears, but more experiments are necessarily needed to verify this initial hypothesis.
Another piece of information is that after the cut-off speed, it is more economical in energy to walk barefoot. Basically, this graph is saying that the more weight you add to your lower limbs the more energy you should expect to spend, a fact that is not an earth shattering observation.
Biomechanics tells us that the structure of our body is not geared toward saving on our muscular effort, but to increasing our range and speed of movements. Most of our muscles are connected to the bones of our limbs and their respective joints in manners they have to exert great effort and many fold the weight of our body members to overcome any of our limb’s mass.
Usually, the tendons of our muscles are inserted to the limb bones, close to the joints, and thus the muscles have to exert a huge effort to overcome the moment of the bone and flesh weight in order to effect a movement. Any extra mass to our limbs will tax our muscles to produce many folds the additional weight.
There is a caveat however; if you wrapped a weight of 1.3 kg around your ankles and walked bare feet you would consume more energy than without the added weight, but the curve would be parallel to the previous curve and not increasing more steepily than walking with shoes weighting 1.3 Kg. Consequently, the variation in the behavior of the graphs result from a combination of added weight and lesser static coefficient of friction exerted by the shoes on the walking surface than the bare foot..
Thus, what this graph does not mention is the static coefficient of friction between the footwear and the ground, and which is the most important variable and in this case, can concatenate many independent and control variables such as the materials of the footwear and the type of ground into a unique independent variable of coefficient of friction.
The higher the coefficient of friction the easier it is to move and progress and thus walking faster for the same amount of effort invested. It is not that important to generate muscle force if the reaction force on the surface cannot be produced to move a person in the right direction. For example, it is extremely difficult to move on slippery surfaces no matter how much muscular effort we generate. Apparently, the shape and skin texture of our foot provide a better and more efficient coefficient of friction than most foot wears.
However, the most important fact of this simple experiment is showing us the behavior of the curves and offering additional hypotheses for other studies.
What this graph is not telling us is the best story of all, and which can excite the mind into further investigation. For example, what kind of earth materials are we walking on; sands, asphalt, rough terrains, slippery roads or grassy fields? Does the sample of bare feet walkers include aboriginals used in walking bare feet, city dwellers, and people from the province? Does the sample includes groups of people according to the softness of their feet skins or the size of feet?
May be the shape of the curves are the same for females as well, but it would be curious to find out the magnitude of variations compared to males. It is clear that a simple and lousy graph delved us into the problems of experimentation and raised enough questions to attend to various fields of knowledge.
In the final analysis, the question is how relevant is this experiment practically? How far can a modern man walk bare feet? Does any economy in energy compensate for the ache, pain and injuries suffered by walking bare feet? Would athletes be allowed to compete bare feet if it is proven to increase performance and break new records? Does anyone care of walking barefoot in order to save a few kilo Joules?
The theme of this article is that you can venture into many fields of knowledge, just by focusing your attention on graphs and tables and permitting your mind to navigate into uncharted waters through queries and critical thinking.
Multidisciplinary view of design
Posted on: October 26, 2008
Article “31 (December 18, 2005)
“A seminar on a multidisciplinary view of design”
The term “designing” is so commonly used that its all encompassing scope has lamentably shrunken in the mind of graduating engineers. This talk attempts to restore the true meaning of design as a multidisciplinary concept that draw its value from the cooperation and inputs of many practitioners in a team.
This is a scenario of a seminar targeting freshmen engineers, who will ultimately be involved in submitting design projects, is meant to orient engineers for a procedure that might provide their design projects the necessary substance for becoming marketable and effective in reducing the pitfalls in having to redesign. The ultimate purpose is to providing the correct designing behavior from the first year.
Answering the following questions might be the basis of acquiring a proper behavior in design projects, which should be carried over in their engineering careers. Many of these questions are never formally asked in the engineering curriculum.
Q1. What is the primary job of an engineer? What does design means? How do you perceive designing to look like?
A1. The discussion should be reopened after setting the tone for the talk and warming up the audience to alternative requirements of good design.
Q2. To whom are you designing? What category of people? Who are your target users? Engineer, consumers, support personnel, operators?
A2. Generate from audience potential design projects as explicit examples to develop on that idea.
Q3. What are your primary criteria in designing? Error free application product? Who commit errors? Can a machine do errors?
A3. Need to explicitly emphasize that error in the design and its usage is the primary criterion and which encompass the other more familiar engineering and business criteria
Q4. How can we categorize errors? Had you any exposure to error taxonomy? Who is at fault when an error is committed or an accident occurs?
A4. Provide a short summary of different error taxonomies; the whole administrative and managerial procedures and hierarchy of the enterprise need also to be investigated.
Q5. Can you foresee errors, near accidents, accidents in your design?
A5. Take a range oven for example, expose the foreseeable errors and accidents in the design, babies misuse and the display and control idiosyncrasy.
Q6. Can we practically account for errors without specific task taxonomy?
A6. Generate a discussion on tasks and be specific on a selected job.
Q7. Do you view yourself as responsible for designing interfaces to your design projects depending on the target users? Would you relinquish your responsibilities for being in the team assigned to designing an interface for your design project? What kinds of interfaces are needed for your design to be used efficiently?
A7. Discuss the various interfaces attached to any design and as prolongement to marketable designs.
Q8. How engineers solve problems? Searching for the applicable formulas? Can you figure out the magnitude of the answer? Have you memorized the allowable range for your answers from the given data and restriction imposed in the problem after solving so many exercises? Have you memorize the dimensions of your design problem?
A8. Figure out the magnitude and the range of the answers before attempting to solve a question; solve algebraically your equations before inputting data; have a good grasp of all the relevant independent variables.
Q9. What are the factors or independent variables that may affect your design project? How can we account for the interactions among the factors?
A9. Offer an exposition to design of experiments
Q10. Have you been exposed to reading research papers? Can you understand, analyze and interpret the research paper data? Can you have an opinion as to the validity of an experiment? Would you accept the results of any peer reviewed article as facts that may be readily applied to your design projects?
A10. Explain the need to be familiar with the procedures and ways of understanding research articles as a continuing education requirement.
Q11. Do you expect to be in charged of designing any new product or program or procedures in your career? Do you view most of your job career as a series of supporting responsibilities; like just applying already designed programs and procedures?
Q12. Are you ready to take elective courses in psychology, sociology, marketing, business targeted to learning how to design experiments and know more about the capabilities, limitations and behavioral trends of target users? Are you planning to go for graduate studies and do you know what elective courses might suit you better in your career?
A12. Taking multidisciplinary courses enhances communication among design team members and more importantly encourages reading research papers in other disciplines related to improving a design project. Designing is a vast and complex concept that requires years of practice and patience to encompass several social science disciplines.
Q13. Can you guess what should have been my profession?
A13. My discipline is Industrial engineering with a major in Human Factors oriented toward designing interfaces for products and systems. Consequently, my major required taking multidisciplinary courses in marketing, psychology and econometrics and mostly targeting various methodologies for designing experiments, collecting data and statistically analyzing gathered data in order to predict system’s behavior.
Adonis Diaries 