nBot, a two wheel balancing robot
nBot Balancing Robot David P. Anderson nBot: 27 January 2003 Update: 14 Sept 2013: nBot Rev 6.0. nBot completely rebuilt as Version 6.0 after the disastrous LiPo Fire of 2012. Update: 14 June 2012: nBot finally has some avoidance and navigation sensors, two PING))) ultrasonic sonar sensors, and five SHARP IR distance detectors, in addition to the MicroStrain FAS-G IMU. Here is a video of nBot doing perimeter following (72M mpeg) in the basement of the building where I work. I've been working on a two-wheeled balancing robot, nBot . This robot was featured as NASA's Cool Robot of the Week for 19 May 2003. The basic idea for a two-wheeled dynamically balancing robot is pretty simple: drive the wheels in the direction that the upper part of the robot is falling. The robot hardware was built in my home machine shop. Rev 1. Rev 2. Rev 3. The ball-bearing pivot and angle sensor were replaced by a piezo-electric gyroscope and ADXL202 accelerometer mounted just above the motor deck. Rev 4.
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Existence
First published Wed Oct 10, 2012 Existence raises deep and important problems in metaphysics, philosophy of language, and philosophical logic. Many of the issues can be organized around the following two questions: Is existence a property of individuals? and Assuming that existence is a property of individuals, are there individuals that lack it? What does it mean to ask if existence is a property? A full answer to this question requires a general theory of properties, which is well beyond the scope of this article. There is a debate in the literature on properties between the abundant conception of properties, according to which there is a property corresponding to every natural language predicate and, more generally, every class of individuals, and the sparse conception of properties, according to which a predicate expresses a property only if the objects that predicate is true of resemble one another in an intrinsic way. 1. Russell's strategy depends on two claims.
Genetic Algorithms links
redemption in a blog Ruby, Rails, Firefox, Anime, Mac Genetic Algorithms links In: Programming 12Aug2003 This is a collection of links on Genetic Algorithms. Genetic Algorithms Warehouse is an excellent resource with collections of links to tutorials, papers, recommended books, software libraries, and essays. that is “study of genetic algorithms and, in particular, of their application in event classification and feature selection”. Darrell Whitley had written a GA tutorial circa 1993. Note to self: * indicates printed in hard copy 6 Responses to Genetic Algorithms links Lead Generation May 24th, 2004 at 7am Direct response agency specializing in lead generation direct marketing per inquiry and cost per action advertising through the Internet, Optin Email, Telemarketing, and Search Engine Optimization. Yuri Burger May 28th, 2004 at 3pm GPL C++ template library of genetic algorithms Perfectionism - the quest for the impossible August 17th, 2003 at 10am Cheah Chu Yeow August 12th, 2003 at 8am Excited?
Psychological ("personality") Types
Psychological ("personality") Types According to Jung's theory of Psychological Types we are all different in fundamental ways. One's ability to process different information is limited by their particular type. These types are sixteen. People can be either Extroverts or Introverts, depending on the direction of their activity ; Thinking, Feeling, Sensing, Intuitive, according to their own information pathways; Judging or Perceiving, depending on the method in which they process received information. Extroverts vs. Extroverts are directed towards the objective world whereas Introverts are directed towards the subjective world. Sensing vs. Sensing is an ability to deal with information on the basis of its physical qualities and its affection by other information. Thinking vs. Thinking is an ability to deal with information on the basis of its structure and its function. Perceiving vs. Perceiving types are motivated into activity by the changes in a situation. ENTp , ISFp , ESFj , INTj , ENFj
Cprogramming.com - Articles - Genetic Algorithms
Genetic algorithms are useful for solving problems having solutions representable as strings (hence the name Genetic Algorithm - the programming model is based on DNA). In terms of practical value, genetic algorithms are useful for solving problems in which the solutions are difficult to find by following a specific algorithm designed to solve the problem (using genetic algorithms in place of predesigned algorithms such as Djikstra's algorithm for path finding just wouldn't make sense). It functions as a sort of systematized brute force approach. Problems genetic algorithms are valuable for solving include scheduling problems, constraint satisfaction problems, and other problems that require searching a large number of possibilities. Genetic algorithms work from the same basis as evolutionary theory. The breeding function generally works by taking slices of each solution and splicing them together into a new one.
Machine Guns and Gun Systems"
In the 1800s, gun manufacturers designed a number of mechanisms to address the problems associated with limited firing ability. A lot of these early machine guns combined several barrels and firing hammers into a single unit. Among the most popular designs was the Gatling gun, named after its inventor Richard Jordan Gatling. This weapon -- the first machine gun to gain widespread popularity -- consists of six to 10 gun barrels positioned in a cylinder. Each barrel has its own breech and firing pin system. Each firing pin has a small cam head that catches hold of a slanted groove in the gun's body. The Gatling gun played an important role in several 19th century battles, but it wasn't until the early 20th century that the machine gun really established itself as a weapon to be reckoned with. The Gatling gun is often considered a machine gun because it shoots a large number of bullets in a short amount of time. Recoil systemsBlowback systemsGas mechanisms
An Introduction to Genetic Algorithms in Python
Do you have a problem that could be solved by iterative optimization? With this excerpt from Programming Collective Intelligence, Toby Segaran explores the use of genetic algorithms to solve such problems using the Python. Another set of techniques for optimization, also inspired by nature, is called genetic algorithms. These work by initially creating a set of random solutions known as the population. Table 5.1. After the solutions are ranked, a new population—known as the next generation—is created. There are two ways that solutions can be modified. Figure 5.3. The other way to modify solutions is called crossover or breeding. Figure 5.4. A new population, usually the same size as the old one, is created by randomly mutating and breeding the best solutions. Add geneticoptimize to optimization.py:
