Insect flight. Original veins and wing posture of a dragonfly Hoverflies hovering to mate Cutout of a butterfly's wing with magnification Hardened forewings and hind wings unfolding in a beetle The physical dynamics of flight are composed of direct and indirect flight. Morphology[edit] Internal[edit] Each of the wings consists of a thin membrane supported by a system of veins. Venation[edit] Venation of insect wings, based on the Comstock–Needham system In some very small insects, the venation may be greatly reduced. The archedictyon is the name given to a hypothetical scheme of wing venation proposed for the very first winged insect. Costa (C) – the leading edge of the wing Subcosta (Sc) – second longitudinal vein (behind the costa), typically unbranched Radius (R) – third longitudinal vein, one to five branches reach the wing margin Media (M) – fourth longitudinal vein, one to four branches reach the wing margin Cubitus (Cu) – fifth longitudinal vein, one to three branches reach the wing margin Fields[edit]
Vehicle dynamics. This article applies primarily to automobiles. For single-track vehicles, specifically the two-wheeled variety, see bicycle and motorcycle dynamics. For aircraft, see aerodynamics. For watercraft see Hydrodynamics. Components[edit] Components, attributes or aspects of vehicle dynamics include: Aerodynamic specific[edit] Some attributes or aspects of vehicle dynamics are purely aerodynamic.
Geometry specific[edit] Some attributes or aspects of vehicle dynamics are purely geometric. Mass specific[edit] Some attributes or aspects of vehicle dynamics are purely due to mass and its distribution. Motion specific[edit] Some attributes or aspects of vehicle dynamics are purely dynamic. Tire specific[edit] Some attributes or aspects of vehicle dynamics can be attributed directly to the tires. Roadway specific[edit] Some attributes or aspects of vehicle dynamics can be attributed directly to the roads on which they travel.
Driving techniques[edit] Analysis and simulation[edit] Techniques include: Hans B. Pacejka. Hans Bastiaan Pacejka (Rotterdam, 1934) is an expert in vehicle system dynamics and particularly in tire dynamics, fields in which his works are now standard references.[1][2] He is Professor emeritus at Delft University of Technology in Delft, Netherlands.[3] The Pacejka "Magic Formula" tire models[edit] Magic Formula Curve The Pacejka tire models are widely used in professional vehicle dynamics simulations, and racing car games, as they are reasonably accurate, easy to program, and solve quickly.[5] A problem with Pacejka's model is that when implemented into computer code, it doesn't work for low speeds (from around the pit-entry speed), because a velocity term in the denominator makes the formula diverge.[6] An alternative to Pacejka tire models are brush tire models, which can be analytically derived, although empirical curve fitting is still required for good correlation,[7][8] and they tend to be less accurate than the MF models.[9] The general form of the magic formula is:
Electronic stability control. Electronic stability control (ESC), also referred to as electronic stability program (ESP) or dynamic stability control (DSC), is a computerized technology [1][2] that improves the safety of a vehicle's stability by detecting and reducing loss of traction (skidding).[3] When ESC detects loss of steering control, it automatically applies the brakes to help "steer" the vehicle where the driver intends to go. Braking is automatically applied to wheels individually, such as the outer front wheel to counter oversteer or the inner rear wheel to counter understeer. Some ESC systems also reduce engine power until control is regained. ESC does not improve a vehicle's cornering performance; instead, it helps to minimize the loss of control. According to Insurance Institute for Highway Safety and the U.S. History[edit] In 1987, the earliest innovators of ESC, Mercedes-Benz and BMW, introduced their first traction control systems.
Introduction[edit] Operation[edit] Effectiveness[edit] Computational geometry. Computational geometry is a branch of computer science devoted to the study of algorithms which can be stated in terms of geometry. Some purely geometrical problems arise out of the study of computational geometric algorithms, and such problems are also considered to be part of computational geometry. Computational complexity is central to computational geometry, with great practical significance if algorithms are used on very large datasets containing tens or hundreds of millions of points. For such sets, the difference between O(n2) and O(n log n) may be the difference between days and seconds of computation. The main impetus for the development of computational geometry as a discipline was progress in computer graphics and computer-aided design and manufacturing (CAD/CAM), but many problems in computational geometry are classical in nature, and may come from mathematical visualization.
The main branches of computational geometry are: Combinatorial computational geometry[edit] Multidisciplinary design optimization - Wikipedia, the free ency. MDO allows designers to incorporate all relevant disciplines simultaneously. The optimum of the simultaneous problem is superior to the design found by optimizing each discipline sequentially, since it can exploit the interactions between the disciplines. However, including all disciplines simultaneously significantly increases the complexity of the problem. These techniques have been used in a number of fields, including automobile design, naval architecture, electronics, architecture, computers, and electricity distribution. However, the largest number of applications have been in the field of aerospace engineering, such as aircraft and spacecraft design.
For example, the proposed Boeing blended wing body (BWB) aircraft concept has used MDO extensively in the conceptual and preliminary design stages. History[edit] Since 1990, the techniques have expanded to other industries. Origins in structural optimization[edit] Gradient-based methods[edit] Non-gradient-based methods[edit] Models[edit]
Monte Carlo method. Monte Carlo methods (or Monte Carlo experiments) are a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results; typically one runs simulations many times over in order to obtain the distribution of an unknown probabilistic entity. They are often used in physical and mathematical problems and are most useful when it is difficult or impossible to obtain a closed-form expression, or infeasible to apply a deterministic algorithm. Monte Carlo methods are mainly used in three distinct problem classes: optimization, numerical integration and generation of draws from a probability distribution.
The modern version of the Monte Carlo method was invented in the late 1940s by Stanislaw Ulam, while he was working on nuclear weapons projects at the Los Alamos National Laboratory. Immediately after Ulam's breakthrough, John von Neumann understood its importance and programmed the ENIAC computer to carry out Monte Carlo calculations. Introduction[edit] Optimization (mathematics) In mathematics, computer science, or management science, mathematical optimization (alternatively, optimization or mathematical programming) is the selection of a best element (with regard to some criteria) from some set of available alternatives.[1] Optimization problems[edit] An optimization problem can be represented in the following way: Sought: an element x0 in A such that f(x0) ≤ f(x) for all x in A ("minimization") or such that f(x0) ≥ f(x) for all x in A ("maximization").
Such a formulation is called an optimization problem or a mathematical programming problem (a term not directly related to computer programming, but still in use for example in linear programming – see History below). Many real-world and theoretical problems may be modeled in this general framework. By convention, the standard form of an optimization problem is stated in terms of minimization. The expression Notation[edit] Optimization problems are often expressed with special notation.
. . , occurring at Similarly, Index (search engine) Popular engines focus on the full-text indexing of online, natural language documents.[1] Media types such as video and audio[2] and graphics[3] are also searchable. Meta search engines reuse the indices of other services and do not store a local index, whereas cache-based search engines permanently store the index along with the corpus. Unlike full-text indices, partial-text services restrict the depth indexed to reduce index size.
Larger services typically perform indexing at a predetermined time interval due to the required time and processing costs, while agent-based search engines index in real time. Indexing[edit] The purpose of storing an index is to optimize speed and performance in finding relevant documents for a search query. Index design factors[edit] Major factors in designing a search engine's architecture include: Merge factors Storage techniques How to store the index data, that is, whether information should be data compressed or filtered.
Index size Lookup speed Maintenance. Predictive analytics. Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future, or otherwise unknown, events.[1][2] In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions.[3] Predictive analytics is used in actuarial science,[4] marketing,[5] financial services,[6] insurance, telecommunications,[7] retail,[8] travel,[9] healthcare,[10] pharmaceuticals[11] and other fields. One of the most well known applications is credit scoring,[1] which is used throughout financial services.
Definition[edit] Types[edit] Predictive models[edit] Descriptive models[edit] Decision models[edit] Applications[edit] Collection analytics[edit] Weka (machine learning) Free availability under the GNU General Public License.Portability, since it is fully implemented in the Java programming language and thus runs on almost any modern computing platform.A comprehensive collection of data preprocessing and modeling techniques.Ease of use due to its graphical user interfaces. Weka supports several standard data mining tasks, more specifically, data preprocessing, clustering, classification, regression, visualization, and feature selection. All of Weka's techniques are predicated on the assumption that the data is available as a single flat file or relation, where each data point is described by a fixed number of attributes (normally, numeric or nominal attributes, but some other attribute types are also supported). Weka provides access to SQL databases using Java Database Connectivity and can process the result returned by a database query.
The Explorer interface features several panels providing access to the main components of the workbench: Neural network. An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one neuron to the input of another. For example, a neural network for handwriting recognition is defined by a set of input neurons which may be activated by the pixels of an input image. After being weighted and transformed by a function (determined by the network's designer), the activations of these neurons are then passed on to other neurons. This process is repeated until finally, an output neuron is activated.
Like other machine learning methods - systems that learn from data - neural networks have been used to solve a wide variety of tasks that are hard to solve using ordinary rule-based programming, including computer vision and speech recognition. Background[edit] There is no single formal definition of what an artificial neural network is. History[edit] and. Arithmetic circuit complexity. In computational complexity theory, arithmetic circuits are the standard model for computing polynomials. Informally, an arithmetic circuit takes as inputs either variables or numbers, and is allowed to either add or multiply two expressions it already computed. Arithmetic circuits give us a formal way for understanding the complexity of computing polynomials. The basic type of question in this line of research is `what is the most efficient way for computing a given polynomial f? '. Definitions[edit] A simple arithmetic circuit. ; in the first case it is a sum gate and in the second a product gate.
A circuit has two complexity measures associated with it: size and depth. An arithmetic circuit computes a polynomial in the following natural way. Overview[edit] Given a polynomial f, we may ask ourselves what is the best way to compute it - for example, what is the smallest size of a circuit computing f. Upper bounds[edit] (this is a depth three circuit).
Lower bounds[edit] Depth reduction[edit] Hermann Schlichting. Hermann Schlichting (22 September 1907 – 15 June 1982) was a German fluid dynamics engineer. Life and work[edit] Hermann Schlichting studied from 1926 till 1930 mathematics, physics and applied mechanics at the University of Jena, Vienne and Göttingen. In 1930 he wrote his PhD in Göttingen titled Über das ebene Windschattenproblem and also in the same year passed the state examination as teacher for higher mathematics and physics. His meeting with Ludwig Prandtl had a long lasting effect on him. He worked from 1931 till 1935 at the Kaiser Wilhelm Institute for Flow Research in Göttingen. After joining in October 1937 Schlichting worked on setting up the Aerodynamic Institute at the Braunschweig-Waggum airport. The transition from laminar to turbulence results in Tollmien–Schlichting waves named after him.
Prof. Achievements[edit] 1953 Medal "50th Anniversary of Powered Flight“ from National Aeronautical Association, Washington D.C. 1968 Dr. 1972 Bundesverdienstkreuz Books[edit] Information graphics. Information graphics or infographics are graphic visual representations of information, data or knowledge intended to present complex information quickly and clearly.[1][2] They can improve cognition by utilizing graphics to enhance the human visual system’s ability to see patterns and trends.[3][4] The process of creating infographics can be referred to as data visualization, information design, or information architecture.[2] Overview[edit] Infographics have been around for many years and recently the proliferation of a number of easy-to-use, free tools have made the creation of infographics available to a large segment of the population.
Social media sites such as Facebook and Twitter have also allowed for individual infographics to be spread among many people around the world. In newspapers, infographics are commonly used to show the weather, as well as maps, site plans, and graphs for statistical data. "Graphical displays should: Graphics reveal data. History[edit] Early[edit] Visualization (computer graphics) - Wikipedia, the free encyclop.