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A technique is presented for obtaining confidence intervals and confidence bands for a one-dimensional response surface which is assumed to be a general-order polynomial.

A technique is presented for obtaining confidence intervals and confidence bands for a one-dimensional response surface which is assumed to be a general-order polynomial.

A technique is presented for obtaining confidence intervals and confidence bands for a one-dimensional response surface which is assumed to be a general-order polynomial.

A technique is presented for obtaining confidence intervals and confidence bands for a one-dimensional response surface which is assumed to be a general-order polynomial.

The report describes three advanced technologies--robotics, artificial intelligence, and computer simulation--and identifies the ways in which they might contribute to special education. A hybrid methodology was employed to identify existing technology and forecast future needs. Following this framework, each of the technologies is defined, considered in terms of the state of the art, discussed according to ratings of scenarios by a panel of technology and special education experts; and predictions are made for the likelihood of those scenarios. Near-term applications are identified: an object and mechanical device manipulating robot to help students perform independently daily living or vocational activities (robotics); a clinical diagnostic expert system to help in screening, diagnosis, and placement (artificial intelligence); and a personal management simulator to teach students about practical living skills (computer simulation). A series of eight conclusions are offered, including that certain applications of artificial intelligence and computer simulation appear more promising, in the foreseeable future, than those in robotics; that some significant technical and cost barriers exist; and that most of these barriers are generic to all applications of the technologies, not solely to special education settings. Appendixes contain summaries of industrial applications for which in-depth reviews were conducted, a questionnaire for expert panelists, instructions to panelists for developing scenarios, and scenarios of technologies in special education. (CL)

The report describes three advanced technologies--robotics, artificial intelligence, and computer simulation--and identifies the ways in which they might contribute to special education. A hybrid methodology was employed to identify existing technology and forecast future needs. Following this framework, each of the technologies is defined, considered in terms of the state of the art, discussed according to ratings of scenarios by a panel of technology and special education experts; and predictions are made for the likelihood of those scenarios. Near-term applications are identified: an object and mechanical device manipulating robot to help students perform independently daily living or vocational activities (robotics); a clinical diagnostic expert system to help in screening, diagnosis, and placement (artificial intelligence); and a personal management simulator to teach students about practical living skills (computer simulation). A series of eight conclusions are offered, including that certain applications of artificial intelligence and computer simulation appear more promising, in the foreseeable future, than those in robotics; that some significant technical and cost barriers exist; and that most of these barriers are generic to all applications of the technologies, not solely to special education settings. Appendixes contain summaries of industrial applications for which in-depth reviews were conducted, a questionnaire for expert panelists, instructions to panelists for developing scenarios, and scenarios of technologies in special education. (CL)

The report describes three advanced technologies--robotics, artificial intelligence, and computer simulation--and identifies the ways in which they might contribute to special education. A hybrid methodology was employed to identify existing technology and forecast future needs. Following this framework, each of the technologies is defined, considered in terms of the state of the art, discussed according to ratings of scenarios by a panel of technology and special education experts; and predictions are made for the likelihood of those scenarios. Near-term applications are identified: an object and mechanical device manipulating robot to help students perform independently daily living or vocational activities (robotics); a clinical diagnostic expert system to help in screening, diagnosis, and placement (artificial intelligence); and a personal management simulator to teach students about practical living skills (computer simulation). A series of eight conclusions are offered, including that certain applications of artificial intelligence and computer simulation appear more promising, in the foreseeable future, than those in robotics; that some significant technical and cost barriers exist; and that most of these barriers are generic to all applications of the technologies, not solely to special education settings. Appendixes contain summaries of industrial applications for which in-depth reviews were conducted, a questionnaire for expert panelists, instructions to panelists for developing scenarios, and scenarios of technologies in special education. (CL)

The report describes three advanced technologies--robotics, artificial intelligence, and computer simulation--and identifies the ways in which they might contribute to special education. A hybrid methodology was employed to identify existing technology and forecast future needs. Following this framework, each of the technologies is defined, considered in terms of the state of the art, discussed according to ratings of scenarios by a panel of technology and special education experts; and predictions are made for the likelihood of those scenarios. Near-term applications are identified: an object and mechanical device manipulating robot to help students perform independently daily living or vocational activities (robotics); a clinical diagnostic expert system to help in screening, diagnosis, and placement (artificial intelligence); and a personal management simulator to teach students about practical living skills (computer simulation). A series of eight conclusions are offered, including that certain applications of artificial intelligence and computer simulation appear more promising, in the foreseeable future, than those in robotics; that some significant technical and cost barriers exist; and that most of these barriers are generic to all applications of the technologies, not solely to special education settings. Appendixes contain summaries of industrial applications for which in-depth reviews were conducted, a questionnaire for expert panelists, instructions to panelists for developing scenarios, and scenarios of technologies in special education. (CL)

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The report is organized in three volumes. The two computer programs presented in Volume II provide some of the basic digital implementation tools required in the analysis and synthesis of hybrid systems. The DISCRET computer program converts a general analog or continuous model expressed in the s-plane to the z-, w-, or w'-plane. DISCRET can calculate the standard, delayed, or advanced discrete transform. Data holds including the zero order, first order, second order, and slewer can be inserted into the transformation. The second program, TXCONV, implements the conversion of a high-rate discrete transform to a low-rate discrete transform. Detailed descriptions of the DISCRET and TXCONV computer programs are provided. This includes the available program options, theoretical basis for the mechanization algorithms, general and detailed program structure, required input data, and typical program output.

The report is organized in three volumes. The two computer programs presented in Volume II provide some of the basic digital implementation tools required in the analysis and synthesis of hybrid systems. The DISCRET computer program converts a general analog or continuous model expressed in the s-plane to the z-, w-, or w'-plane. DISCRET can calculate the standard, delayed, or advanced discrete transform. Data holds including the zero order, first order, second order, and slewer can be inserted into the transformation. The second program, TXCONV, implements the conversion of a high-rate discrete transform to a low-rate discrete transform. Detailed descriptions of the DISCRET and TXCONV computer programs are provided. This includes the available program options, theoretical basis for the mechanization algorithms, general and detailed program structure, required input data, and typical program output.

From the bitsavers.org collection, a scanned-in computer-related document. trw :: trw-130 :: M250-2U15 Computer Simulation Applications Study May62

About a year before Ken Krieg left his position as under secretary of defense for acquisition, technology and logistics, he challenged the Defense Acquisition University to take a more concentrated review of team training in the defense acquisition workforce. With additional team training specifi cally focused on sharpening intact teams, he felt the defense acquisition workforce could operate at even higher performance levelssomething the Department of Defense commonly demands. He even codifi ed it as a near-term training goal in the 2007 AT and L Human Capital Strategic Plan, V3.0, under Workforce Goal 4.2.3, Pilot an initial unitcohort training program. He also encouraged the use of state-of-the-art simulation technology.

Techniques are developed to estimate power gain, delay, signal-to-noise ratio, and mean square error in digital computer simulations of lowpass and bandpass systems. The techniques are applied to analog and digital communications. The signal-to-noise ratio estimates are shown to be maximum likelihood estimates in additive white Gaussian noise. The methods are seen to be especially useful for digital communication systems where the mapping from the signal-to-noise ratio to the error probability can be obtained. Simulation results show the techniques developed to be accurate and quite versatile in evaluating the performance of many systems through digital computer simulation.

A description and user's guide of the computer program developed to simulate the dynamics of an electromechanical actuator for aerospace applications are presented. The effects of the stator phase currents on the permanent magnets of the rotor are examined. The voltage and current waveforms present in the power conditioner network during the motoring, regenerative braking, and plugging modes of operation are presented and discussed.

This is a real-time robotic controller and simulator which is a MIMD-SIMD parallel architecture for interfacing with an external host computer and providing a high degree of parallelism in computations for robotic control and simulation. It includes a host processor for receiving instructions from the external host computer and for transmitting answers to the external host computer. There are a plurality of SIMD microprocessors, each SIMD processor being a SIMD parallel processor capable of exploiting fine grain parallelism and further being able to operate asynchronously to form a MIMD architecture. Each SIMD processor comprises a SIMD architecture capable of performing two matrix-vector operations in parallel while fully exploiting parallelism in each operation. There is a system bus connecting the host processor to the plurality of SIMD microprocessors and a common clock providing a continuous sequence of clock pulses. There is also a ring structure interconnecting the plurality of SIMD microprocessors and connected to the clock for providing the clock pulses to the SIMD microprocessors and for providing a path for the flow of data and instructions between the SIMD microprocessors. The host processor includes logic for controlling the RRCS by interpreting instructions sent by the external host computer, decomposing the instructions into a series of computations to be performed by the SIMD microprocessors, using the system bus to distribute associated data among the SIMD microprocessors, and initiating activity of the SIMD microprocessors to perform the computations on the data by procedure call.

In this review we focus on the determination of phase diagrams by computer simulation with particular attention to the fluid-solid and solid-solid equilibria. The calculation of the free energy of solid phases using the Einstein crystal and Einstein molecule methods are described in detail. It is shown that for the hard spheres solid both methods yield the same results and that free energies of solid phases present noticeable finite size effects. Finite size corrections can be introduced, although in an approximate way, to correct for the dependence of the free energy on the size of the system. The computation of free energies of solid phases can be extended to molecular fluids. The procedure to compute free energies of solid phases of water (ices Ih, II, III, IV, V, VI, VII, VIII, IX, XI and XII) using the SPC/E and TIP4P models will be described.Other methods to estimate the melting point of a solid, as the direct fluid-solid coexistence or simulations of the free surface of the solid will be discussed. It will be shown that the melting points of ice Ih for several water models, obtained from these two methods and from free energy calculations agree within statistical uncertainty. Phase diagram calculations can help to improve potential models of molecular fluids; for water, the TIP4P/2005 model can be regarded as an improved version of TIP4P. We will also review some recent work on the phase diagram of the simplest ionic model, the restricted primitive model. Although originally devised to describe ionic liquids, the model is becoming quite popular to describe charged colloids. Besides the possibility of obtaining fluid-solid equilibria for simple protein models will be discussed. In these primitive models, the protein is described by a spherical potential with certain anisotropic bonding sites.