Force and Motion a Science A–Z Physical Series Word Count: 1,746
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Glossary Physics (I-Introduction)
1 Glossary Physics (I-introduction) - Efficiency: The percent of the work put into a machine that is converted into useful work output; = work done / energy used [-]. = eta In machines: The work output of any machine cannot exceed the work input (<=100%); in an ideal machine, where no energy is transformed into heat: work(input) = work(output), =100%. Energy: The property of a system that enables it to do work. Conservation o. E.: Energy cannot be created or destroyed; it may be transformed from one form into another, but the total amount of energy never changes. Equilibrium: The state of an object when not acted upon by a net force or net torque; an object in equilibrium may be at rest or moving at uniform velocity - not accelerating. Mechanical E.: The state of an object or system of objects for which any impressed forces cancels to zero and no acceleration occurs. Dynamic E.: Object is moving without experiencing acceleration. Static E.: Object is at rest.F Force: The influence that can cause an object to be accelerated or retarded; is always in the direction of the net force, hence a vector quantity; the four elementary forces are: Electromagnetic F.: Is an attraction or repulsion G, gravit. const.6.672E-11[Nm2/kg2] between electric charges: d, distance [m] 2 2 2 2 F = 1/(40) (q1q2/d ) [(CC/m )(Nm /C )] = [N] m,M, mass [kg] Gravitational F.: Is a mutual attraction between all masses: q, charge [As] [C] 2 2 2 2 F = GmM/d [Nm /kg kg 1/m ] = [N] 0, dielectric constant Strong F.: (nuclear force) Acts within the nuclei of atoms: 8.854E-12 [C2/Nm2] [F/m] 2 2 2 2 2 F = 1/(40) (e /d ) [(CC/m )(Nm /C )] = [N] , 3.14 [-] Weak F.: Manifests itself in special reactions among elementary e, 1.60210 E-19 [As] [C] particles, such as the reaction that occur in radioactive decay. -
Forces Different Types of Forces
Forces and motion are a part of your everyday life for example pushing a trolley, a horse pulling a rope, speed and acceleration. Force and motion causes objects to move but also to stay still. Motion is simply a movement but needs a force to move. There are 2 types of forces, contact forces and act at a distance force. Forces Every day you are using forces. Force is basically push and pull. When you push and pull you are applying a force to an object. If you are Appling force to an object you are changing the objects motion. For an example when a ball is coming your way and then you push it away. The motion of the ball is changed because you applied a force. Different Types of Forces There are more forces than push or pull. Scientists group all these forces into two groups. The first group is contact forces, contact forces are forces when 2 objects are physically interacting with each other by touching. The second group is act at a distance force, act at a distance force is when 2 objects that are interacting with each other but not physically touching. Contact Forces There are different types of contact forces like normal Force, spring force, applied force and tension force. Normal force is when nothing is happening like a book lying on a table because gravity is pulling it down. Another contact force is spring force, spring force is created by a compressed or stretched spring that could push or pull. Applied force is when someone is applying a force to an object, for example a horse pulling a rope or a boy throwing a snow ball. -
Geodesic Distance Descriptors
Geodesic Distance Descriptors Gil Shamai and Ron Kimmel Technion - Israel Institute of Technologies [email protected] [email protected] Abstract efficiency of state of the art shape matching procedures. The Gromov-Hausdorff (GH) distance is traditionally used for measuring distances between metric spaces. It 1. Introduction was adapted for non-rigid shape comparison and match- One line of thought in shape analysis considers an ob- ing of isometric surfaces, and is defined as the minimal ject as a metric space, and object matching, classification, distortion of embedding one surface into the other, while and comparison as the operation of measuring the discrep- the optimal correspondence can be described as the map ancies and similarities between such metric spaces, see, for that minimizes this distortion. Solving such a minimiza- example, [13, 33, 27, 23, 8, 3, 24]. tion is a hard combinatorial problem that requires pre- Although theoretically appealing, the computation of computation and storing of all pairwise geodesic distances distances between metric spaces poses complexity chal- for the matched surfaces. A popular way for compact repre- lenges as far as direct computation and memory require- sentation of functions on surfaces is by projecting them into ments are involved. As a remedy, alternative representa- the leading eigenfunctions of the Laplace-Beltrami Opera- tion spaces were proposed [26, 22, 15, 10, 31, 30, 19, 20]. tor (LBO). When truncated, the basis of the LBO is known The question of which representation to use in order to best to be the optimal for representing functions with bounded represent the metric space that define each form we deal gradient in a min-max sense. -
Motion and Time Study the Goals of Motion Study
Motion and Time Study The Goals of Motion Study • Improvement • Planning / Scheduling (Cost) •Safety Know How Long to Complete Task for • Scheduling (Sequencing) • Efficiency (Best Way) • Safety (Easiest Way) How Does a Job Incumbent Spend a Day • Value Added vs. Non-Value Added The General Strategy of IE to Reduce and Control Cost • Are people productive ALL of the time ? • Which parts of job are really necessary ? • Can the job be done EASIER, SAFER and FASTER ? • Is there a sense of employee involvement? Some Techniques of Industrial Engineering •Measure – Time and Motion Study – Work Sampling • Control – Work Standards (Best Practices) – Accounting – Labor Reporting • Improve – Small group activities Time Study • Observation –Stop Watch – Computer / Interactive • Engineering Labor Standards (Bad Idea) • Job Order / Labor reporting data History • Frederick Taylor (1900’s) Studied motions of iron workers – attempted to “mechanize” motions to maximize efficiency – including proper rest, ergonomics, etc. • Frank and Lillian Gilbreth used motion picture to study worker motions – developed 17 motions called “therbligs” that describe all possible work. •GET G •PUT P • GET WEIGHT GW • PUT WEIGHT PW •REGRASP R • APPLY PRESSURE A • EYE ACTION E • FOOT ACTION F • STEP S • BEND & ARISE B • CRANK C Time Study (Stopwatch Measurement) 1. List work elements 2. Discuss with worker 3. Measure with stopwatch (running VS reset) 4. Repeat for n Observations 5. Compute mean and std dev of work station time 6. Be aware of allowances/foreign element, etc Work Sampling • Determined what is done over typical day • Random Reporting • Periodic Reporting Learning Curve • For repetitive work, worker gains skill, knowledge of product/process, etc over time • Thus we expect output to increase over time as more units are produced over time to complete task decreases as more units are produced Traditional Learning Curve Actual Curve Change, Design, Process, etc Learning Curve • Usually define learning as a percentage reduction in the time it takes to make a unit. -
Force Powers
FORCE POWERS Compiled by Cheshire Edited by Thiago S. Aranha Table of Contents Jedi Powers Control and Sense Powers Farseeing 17 Control Powers Life Bond 17 Absorb/Dissipate Energy 4 Lightsaber Combat 18 Accelerate Healing 4 Projective Telepathy 19 Concentration 4 Contort/Escape 5 Control Disease 5 Control and Alter Powers Accelerate Another’s Healing 20 Control Pain 5 Control Another’s Disease 20 Detoxify Poison 6 Control Another’s Pain 20 Emptiness 6 Control Breathing 20 Enhance Attribute 7 Detoxify Another’s Poison 20 Force of Will 7 Place Another in Hibernation Trance 21 Hibernation Trance 8 Remove Another’s Fatigue 21 Instinctive Astrogation Control 8 Return Another to Consciousness 21 Reduce Injury 8 Transfer Force 21 Remain Conscious 8 Remove Fatigue 9 Resist Stun 9 Sense and Alter Powers Short-Term Memory Enhancement 9 Dim Another’s Senses 22 Lesser Force Shield 22 Sense Powers Beast Languages 10 Control, Sense and Alter Powers Combat Sense 10 Affect Mind 22 Danger Sense 10 Battle Meditation 23 Instinctive Astrogation 11 Enhanced Coordination 23 Life Detection 11 Force Harmony 24 Life Sense 11 Projected Fighting 24 Life Web 12 Magnify Senses 12 Merge Senses 12 Dark Side Powerss Postcognition 13 Predict Natural Disaster 13 Control Powers Rage 25 Receptive Telepathy 13 Sense Force 14 Sense Force Potential 14 Alter Powers Sense Path 15 Bolt of Hatred 25 Shift Sense 15 Dark Side Web 26 Translation 16 Injure/Kill 26 Weather Sense 16 Control and Alter Powers Alter Powers Aura of Uneasiness 26 Telekinesis 16 Electronic Manipulation 26 -
Robust Topological Inference: Distance to a Measure and Kernel Distance
Journal of Machine Learning Research 18 (2018) 1-40 Submitted 9/15; Revised 2/17; Published 4/18 Robust Topological Inference: Distance To a Measure and Kernel Distance Fr´ed´ericChazal [email protected] Inria Saclay - Ile-de-France Alan Turing Bldg, Office 2043 1 rue Honor´ed'Estienne d'Orves 91120 Palaiseau, FRANCE Brittany Fasy [email protected] Computer Science Department Montana State University 357 EPS Building Montana State University Bozeman, MT 59717 Fabrizio Lecci [email protected] New York, NY Bertrand Michel [email protected] Ecole Centrale de Nantes Laboratoire de math´ematiquesJean Leray 1 Rue de La Noe 44300 Nantes FRANCE Alessandro Rinaldo [email protected] Department of Statistics Carnegie Mellon University Pittsburgh, PA 15213 Larry Wasserman [email protected] Department of Statistics Carnegie Mellon University Pittsburgh, PA 15213 Editor: Mikhail Belkin Abstract Let P be a distribution with support S. The salient features of S can be quantified with persistent homology, which summarizes topological features of the sublevel sets of the dis- tance function (the distance of any point x to S). Given a sample from P we can infer the persistent homology using an empirical version of the distance function. However, the empirical distance function is highly non-robust to noise and outliers. Even one outlier is deadly. The distance-to-a-measure (DTM), introduced by Chazal et al. (2011), and the ker- nel distance, introduced by Phillips et al. (2014), are smooth functions that provide useful topological information but are robust to noise and outliers. Chazal et al. (2015) derived concentration bounds for DTM. -
1 the Principle of Wave–Particle Duality: an Overview
3 1 The Principle of Wave–Particle Duality: An Overview 1.1 Introduction In the year 1900, physics entered a period of deep crisis as a number of peculiar phenomena, for which no classical explanation was possible, began to appear one after the other, starting with the famous problem of blackbody radiation. By 1923, when the “dust had settled,” it became apparent that these peculiarities had a common explanation. They revealed a novel fundamental principle of nature that wascompletelyatoddswiththeframeworkofclassicalphysics:thecelebrated principle of wave–particle duality, which can be phrased as follows. The principle of wave–particle duality: All physical entities have a dual character; they are waves and particles at the same time. Everything we used to regard as being exclusively a wave has, at the same time, a corpuscular character, while everything we thought of as strictly a particle behaves also as a wave. The relations between these two classically irreconcilable points of view—particle versus wave—are , h, E = hf p = (1.1) or, equivalently, E h f = ,= . (1.2) h p In expressions (1.1) we start off with what we traditionally considered to be solely a wave—an electromagnetic (EM) wave, for example—and we associate its wave characteristics f and (frequency and wavelength) with the corpuscular charac- teristics E and p (energy and momentum) of the corresponding particle. Conversely, in expressions (1.2), we begin with what we once regarded as purely a particle—say, an electron—and we associate its corpuscular characteristics E and p with the wave characteristics f and of the corresponding wave. -
Hydraulics Manual Glossary G - 3
Glossary G - 1 GLOSSARY OF HIGHWAY-RELATED DRAINAGE TERMS (Reprinted from the 1999 edition of the American Association of State Highway and Transportation Officials Model Drainage Manual) G.1 Introduction This Glossary is divided into three parts: · Introduction, · Glossary, and · References. It is not intended that all the terms in this Glossary be rigorously accurate or complete. Realistically, this is impossible. Depending on the circumstance, a particular term may have several meanings; this can never change. The primary purpose of this Glossary is to define the terms found in the Highway Drainage Guidelines and Model Drainage Manual in a manner that makes them easier to interpret and understand. A lesser purpose is to provide a compendium of terms that will be useful for both the novice as well as the more experienced hydraulics engineer. This Glossary may also help those who are unfamiliar with highway drainage design to become more understanding and appreciative of this complex science as well as facilitate communication between the highway hydraulics engineer and others. Where readily available, the source of a definition has been referenced. For clarity or format purposes, cited definitions may have some additional verbiage contained in double brackets [ ]. Conversely, three “dots” (...) are used to indicate where some parts of a cited definition were eliminated. Also, as might be expected, different sources were found to use different hyphenation and terminology practices for the same words. Insignificant changes in this regard were made to some cited references and elsewhere to gain uniformity for the terms contained in this Glossary: as an example, “groundwater” vice “ground-water” or “ground water,” and “cross section area” vice “cross-sectional area.” Cited definitions were taken primarily from two sources: W.B. -
Mathematics of Distances and Applications
Michel Deza, Michel Petitjean, Krassimir Markov (eds.) Mathematics of Distances and Applications I T H E A SOFIA 2012 Michel Deza, Michel Petitjean, Krassimir Markov (eds.) Mathematics of Distances and Applications ITHEA® ISBN 978-954-16-0063-4 (printed); ISBN 978-954-16-0064-1 (online) ITHEA IBS ISC No.: 25 Sofia, Bulgaria, 2012 First edition Printed in Bulgaria Recommended for publication by The Scientific Council of the Institute of Information Theories and Applications FOI ITHEA The papers accepted and presented at the International Conference “Mathematics of Distances and Applications”, July 02-05, 2012, Varna, Bulgaria, are published in this book. The concept of distance is basic to human experience. In everyday life it usually means some degree of closeness of two physical objects or ideas, i.e., length, time interval, gap, rank difference, coolness or remoteness, while the term metric is often used as a standard for a measurement. Except for the last two topics, the mathematical meaning of those terms, which is an abstraction of measurement, is considered. Distance metrics and distances have now become an essential tool in many areas of Mathematics and its applications including Geometry, Probability, Statistics, Coding/Graph Theory, Clustering, Data Analysis, Pattern Recognition, Networks, Engineering, Computer Graphics/Vision, Astronomy, Cosmology, Molecular Biology, and many other areas of science. Devising the most suitable distance metrics and similarities, to quantify the proximity between objects, has become a standard task for many researchers. Especially intense ongoing search for such distances occurs, for example, in Computational Biology, Image Analysis, Speech Recognition, and Information Retrieval. For experts in the field of mathematics, information technologies as well as for practical users. -
Topology Distance: a Topology-Based Approach for Evaluating Generative Adversarial Networks
Topology Distance: A Topology-Based Approach For Evaluating Generative Adversarial Networks Danijela Horak 1 Simiao Yu 1 Gholamreza Salimi-Khorshidi 1 Abstract resemble real images Xr, while D discriminates between Xg Automatic evaluation of the goodness of Gener- and Xr. G and D are trained by playing a two-player mini- ative Adversarial Networks (GANs) has been a max game in a competing manner. Such novel adversarial challenge for the field of machine learning. In training process is a key factor in GANs’ success: It im- this work, we propose a distance complementary plicitly defines a learnable objective that is flexibly adaptive to existing measures: Topology Distance (TD), to various complicated image-generation tasks, in which it the main idea behind which is to compare the would be difficult or impossible to explicitly define such an geometric and topological features of the latent objective. manifold of real data with those of generated data. One of the biggest challenges in the field of generative More specifically, we build Vietoris-Rips com- models – including for GANs – is the automatic evaluation plex on image features, and define TD based on of the goodness of such models (e.g., whether or not the the differences in persistent-homology groups of data generated by such models are similar to the data they the two manifolds. We compare TD with the were trained on). Unlike supervised learning, where the most commonly-used and relevant measures in goodness of the models can be assessed by comparing their the field, including Inception Score (IS), Frechet´ predictions with the actual labels, or in some other deep- Inception Distance (FID), Kernel Inception Dis- learning models where the goodness of the model can be tance (KID) and Geometry Score (GS), in a range assessed using the likelihood of the validation data under the of experiments on various datasets. -
Newtonian Mechanics Is Most Straightforward in Its Formulation and Is Based on Newton’S Second Law
CLASSICAL MECHANICS D. A. Garanin September 30, 2015 1 Introduction Mechanics is part of physics studying motion of material bodies or conditions of their equilibrium. The latter is the subject of statics that is important in engineering. General properties of motion of bodies regardless of the source of motion (in particular, the role of constraints) belong to kinematics. Finally, motion caused by forces or interactions is the subject of dynamics, the biggest and most important part of mechanics. Concerning systems studied, mechanics can be divided into mechanics of material points, mechanics of rigid bodies, mechanics of elastic bodies, and mechanics of fluids: hydro- and aerodynamics. At the core of each of these areas of mechanics is the equation of motion, Newton's second law. Mechanics of material points is described by ordinary differential equations (ODE). One can distinguish between mechanics of one or few bodies and mechanics of many-body systems. Mechanics of rigid bodies is also described by ordinary differential equations, including positions and velocities of their centers and the angles defining their orientation. Mechanics of elastic bodies and fluids (that is, mechanics of continuum) is more compli- cated and described by partial differential equation. In many cases mechanics of continuum is coupled to thermodynamics, especially in aerodynamics. The subject of this course are systems described by ODE, including particles and rigid bodies. There are two limitations on classical mechanics. First, speeds of the objects should be much smaller than the speed of light, v c, otherwise it becomes relativistic mechanics. Second, the bodies should have a sufficiently large mass and/or kinetic energy. -
Motion and Forces Notes Key Speed • to Describe How Fast Something Is Traveling, You Have to Know Two Things About Its Motion
Motion and Forces Notes Key Speed • To describe how fast something is traveling, you have to know two things about its motion. • One is the distance it has traveled, or how far it has gone. • The other is how much time it took to travel that distance. Average Speed • To calculate average speed, divide the distance traveled by the time it takes to travel that distance. • Speed Equation Average speed (in m/s) = distance traveled (in m) divided by time (in s) it took to travel the distance s = d t • Because average speed is calculated by dividing distance by time, its units will always be a distance unit divided by a time unit. • For example, the average speed of a bicycle is usually given in meters per second. • Average speed is useful if you don't care about the details of the motion. • When your motion is speeding up and slowing down, it might be useful to know how fast you are going at a certain time. Instantaneous Speed • The instantaneous speed is the speed of an object at any instant of time. Constant Speed • Sometimes an object is moving such that its instantaneous speed doesn’t change. • When the instantaneous speed doesn't change, an object is moving with constant speed. • The average speed and the instantaneous speed are the same. Calculating Distance • If an object is moving with constant speed, then the distance it travels over any period of time can be calculated using the equation for average speed. • When both sides of this equation are multiplied by the time, you have the equation for distance.