This paper presents new methods for understanding specific building performance trends and their dependencies by parameterizing digital building models over multiple variables in order to create a high dimensional des...
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This paper presents new methods for understanding specific building performance trends and their dependencies by parameterizing digital building models over multiple variables in order to create a high dimensional design space that can be rapidly simulated, analyzed, and visualized. These methods allow for the potential delineation of multiple areas of the design space that are characterized by relatively high performance instead of a single optimum. The ability to identify clusters of high performance is particularly helpful to architectural design processes, which must take into consideration variables that cannot be parameterized and resist quantitative methods of optimization.
作者:
John MaedaSteven HellerTim HooverHas been a design partner at Kleiner Perkins Caufield & Byers since January 2014
helping entrepreneurs and portfolio companies to build design into their company cultures. He served as the president of Rhode Island School of Design for six years through 2013 and also served as an associate director of research at the MIT Media Lab. Maeda has been a practicing designer since 1990 and his work is in the permanent collections of the Museum of Modern Art San Francisco Museum of Modern Art and the Cartier Foundation. He serves on the boards of several corporations including Sonos and Wieden + Kennedy and is chair of eBay's Design Advisory Board. His four published books include The Laws of Simplicity and Redesigning Leadership. Maeda is the recipient of a variety of international awards for his creative work including induction into the Art Director's Club Hall of Fame. In 2001 he received the White House National Design Award in 2002
he received the Mainichi Design Prize in Japan and in 2005
he was awarded the Raymond Loewy Foundation Prize in Germany. Maeda earned a BS and an MS from MIT in computer science and electrical engineering. He received a PhD in design science from the University of Tsukuba Institute of Art and Design in Japan as well as an MBA from Arizona State University. Co‐chair and co‐founder of the MFA Designer as Author and Entrepreneur program at the School of Visual Arts
in New York. He was the art director of The New York Times Book Review and now writes the VISUALS column for the NYTBR. He also writes “The Daily Heller” at *** a weekly online design column for Atlantic magazine. He is author co‐author and editor of more than 165 books on the history and practice of graphic design illustration and satiric art. His forthcoming books include 100 Classic Graphic Design Magazines (Laurence King Publishers) and Raw Data: The Process Behind Information Graphics (Thames + Hudson). Heller was the 2011 recipient of the Smithsonian National Design Award for Design Mind. He has also writ
John Maeda is a past president of Rhode Island School of Design and an internationally known designer. He's now involved in helping entrepreneurs build design into their company cultures. Writer and educator Steve...
John Maeda is a past president of Rhode Island School of Design and an internationally known designer. He's now involved in helping entrepreneurs build design into their company cultures. Writer and educator Steven Heller pairs up with Tim Hoover, head of product and design at Canary, to talk about designer CEOs, start‐ups, and cutting out the middleman.
The Kelvin Probe Force Microscopy (KPFM) is a method to detect the surface potential of micro- and nanostructured samples using a common Scanning Probe Microscope (SPM). The electrostatic force has a very long range c...
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This paper presents a semiautomatic method for the identification of immunohistochemical (IHC) staining in digitized samples. The user trains the system by selecting on a sample image some typical positive stained reg...
In this paper we introduce a new approach to characterizing image quality: visual equivalence, Images are visually equivalent if they convey the same information about object appearance even if they are visibly differ...
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ISBN:
(纸本)9780892082810
In this paper we introduce a new approach to characterizing image quality: visual equivalence, Images are visually equivalent if they convey the same information about object appearance even if they are visibly different. In a series of psychophysical experiments we explore how object geometry, material, and illumination interact to produce images that are visually equivalent, and we identify how two kinds of transformations on illumination fields (blurring and warping) influence observers' judgments of equivalence. We use the results of the experiments to derive metrics that can serve as visual equivalence predictors (VEPs) and we generalize these metrics so they can be applied to novel objects and scenes. Finally we validate the predictors in a confirmatory study, and show that they reliably predict observer's judgments of equivalence. Visual equivalence is a significant new approach to measuring image quality that goes beyond existing visible difference metrics by leveraging the fact that some kinds of image differences do not matter to human observers. By taking advantage of higher order aspects of visual object coding, visual equivalence metrics should enable the development of powerful new classes of image capture, compression, rendering, and display algorithms.
Lane detection is an essential component of autonomous mobile robot applications. Any lane detection method has to deal with the varying conditions of the lane and surrounding that the robot would encounter while movi...
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Lane detection is an essential component of autonomous mobile robot applications. Any lane detection method has to deal with the varying conditions of the lane and surrounding that the robot would encounter while moving. Lane detection procedure can provide estimates for the position and orientation of the robot within the lane and also can provide a reference system for locating other obstacles in the path of the robot. In this paper we present a method for lane detection in video frames of a camera mounted on top of the mobile robot. Given video input from the camera, the gradient of the current lane in the near field of view are automatically detected. Randomized Hough Transform is used for extracting parametric curves from the images acquired. A priori knowledge of the lane position is assumed for better accuracy of lane detection.
The measurement of affect in HCI research is a challenging and complex issue. Although a number of techniques for measuring affect have been developed, a systematic discussion of their effectiveness and applicability ...
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The measurement of affect in HCI research is a challenging and complex issue. Although a number of techniques for measuring affect have been developed, a systematic discussion of their effectiveness and applicability in different contexts remains lacking, especially in social contexts with multiple users. As computing shifts to increasingly collaborative and ubiquitous models, it is important to discuss affect measurement beyond the individual level. This workshop will provide a forum where designers, practitioners, and researchers can 1) introduce novel methods of affect measurement that go beyond physiological and self-report measures, 2) advance our understanding of existing measurement methods and how they can be expanded, and 3) critically evaluate issues of affect measurement.
This paper addresses light transport through a discrete random medium, which we define as a volume filled with macroscopic scattering geometry generated by a random process. This formulation is more general than stand...
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Microfacet models have proven very successful for modeling light reflection from rough surfaces. In this paper we review microfacet theory and demonstrate how it can be extended to simulate transmission through rough ...
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