曝光臺 注意防騙
網曝天貓店富美金盛家居專營店坑蒙拐騙欺詐消費者
y = 0.005x-0.1149
R2 = 0.6906
0.0001
0.001
0.01
1 10 100 1,000 10,000 100,000 1,000,000
No. of Coverages to Failure
Vertical Subgrade Strain, inch/inch
Stockton - 8
MWHGL - 7
Structural Layers Study - 6
NAPTF - 9
CC1-MFC-4W
CC1-MFC-6W
CC3-LFC1-6W
CC3-LFC1-4W
CC3-LFC2-6W
CC3-LFC2-4W
CC3-LFC3-6W
CC3-LFC3-4W
CC1-MFS-4W
CC1-MFS-6W
LEDFAA 1.3
Failure Model
FIGURE 3 LEDFAA 1.3 failure model showing full-scale test data and separate model curves for high and
low coverages.
11
h
2
h
2
w + h
t
tires surface
top of subgrade
subgrade
w w
1
2
h
FIGURE 4 Equivalent tire width at the top of the subgrade with no overlap (two equivalent tires).
12
t
tires surface
top of subgrade
subgrade
w w
h
2
h
2
w + t
w + t + h
1
2
h
FIGURE 5 Equivalent tire width at the top of the subgrade with overlap (one equivalent tire).
13
0
1
2
net tandem spacing b
Tandem Gear Factor
h /2 h
FIGURE 6 Tandem gear factor as a function of tandem spacing for two wheels in tandem (the maximum
gear factor is the number of wheels in tandem for other gears, e.g. 3 for a triple-dual-tandem such as a
B-777).
14
FIGURE 7 Vertical subgrade strain evaluation points for B-747-400, all 16 wheels contribute to the strain
computation at each evaluation point.
15
FIGURE 8 Vertical subgrade strain evaluation points for A380-800, all 20 wheels contribute to the strain
computation at each evaluation point.
Organizing and Searching the World Wide Web of Facts Step
Two: Harnessing the Wisdom of the Crowds
Marius Pas¸ca
Google Inc.
1600 Amphitheatre Parkway
Mountain View, California 94043
mars@google.com
ABSTRACT
As part of a large e ort to acquire large repositories of facts
from unstructured text on the Web, a seed-based frame-
work for textual information extraction allows for weakly
supervised extraction of class attributes (e.g., side e ects
and generic equivalent for drugs) from anonymized query
logs. The extraction is guided by a small set of seed at-
tributes, without any need for handcrafted extraction pat-
terns or further domain-speci c knowledge. The attributes
of classes pertaining to various domains of interest to Web
search users have accuracy levels signi cantly exceeding cur-
rent state of the art. Inherently noisy search queries are
shown to be a highly valuable, albeit unexplored, resource
for Web-based information extraction, in particular for the
task of class attribute extraction.
Categories and Subject Descriptors
H.3.1 [Information Storage and Retrieval]: Content
Analysis and Indexing; I.2.7 [Arti cial Intelligence]: Nat-
ural Language Processing; I.2.6 [Arti cial Intelligence]:
Learning; H.3.3 [Information Storage and Retrieval]:
Information Search and Retrieval
General Terms
Algorithms, Experimentation
Keywords
Knowledge acquisition, class attributes, named entities, fact
extraction, Web search queries, unstructured text
1. INTRODUCTION
1.1 Background
Although the information in large textual collections such
as the Web is available in the form of individual textual
documents, the human knowledge encoded within the doc-
uments can be seen as a hidden, implicit Web of classes of
objects (e.g., named entities), interconnected by relations
applying to those objects (e.g., facts). The acquisition of an
extensive World Wide Web of facts from textual documents
is an e ort to improve Web search [12] that also ts into the
Copyright is held by the International World Wide Web Conference Committee
(IW3C2). Distribution of these papers is limited to classroom use,
and personal use by others.
WWW 2007, May 8–12, 2007, Banff, Alberta, Canada.
ACM 9781595936547/
07/0005.
far-reaching goal of automatically constructing knowledge
bases from unstructured text [17].
Recent work on large-scale information extraction holds
much promise, as it scales well to Web-sized text collections
through to an emphasis on lightweight methods for extract-
ing facts of a pre-de ned type (e.g., InstanceOf [15], Person-
AuthorOf-Invention [10] or Country-CapitalOf-City [2]). Be-
yond algorithmic di erences and choice of underlying re-
sources, these methods take as input a small set of facts of a
pre-speci ed type, and mine a textual document collection
to acquire many other facts of the same type. The resulting
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