203,308 research outputs found
Projectverslag E-merge OP 5.7. Ontwikkeling en disseminatie van een didactisch model voor blended learning
No full textPeursen, W. van, Jacobs, F., Oueslati, A., Philipsen, V., Wagenaar, S., Jonge, M. de & Eijl, P.J. van (2007). Omvorming van een bestaande face-to-face cursus naar blended learning. Seminar syllabus. Uitgave E-Merge OP 5.7, Universiteit Leiden, 8 blz. Internet 21 januari 2008: http://hive.e-mergeconsortium.nl/cgi-bin/hive/hive.cgi/Projectverslag_OP_5.7_def_website.pdf?HIVE_REF=hdi%3A4662&HIVE_RET=ORG&HIVE_REQ=2001&HIVE_PROD=0/Projectverslag_OP_5.7_def_website.pd
Long and short term influence of solar activity on galactic cosmic rays with the PAMELA experiment
No full textThis thesis is devoted to the study of the role of the Sun in the cosmic ray’s flux at Earth with the PAMELA experiment. PAMELA is a space borne experiment mounted on the Resurs DK-1 russian satellite, launched in a polar orbit (inclination 70◦) with and altitude between 350 and 610 km (now spherical at ' 580 km) on the 15th June 2006 from the Bajkonur Cosmodrome
A Hypersequent Calculus for Łukasiewicz Logic without the Merge Rule
No full textwere the conclusion of an S rule, then the rule could be applied a third time. We do not have a proof that the rewriting rules terminate. So the admissibility of the M rule (and thus the equivalence of GL/2 to GL/) is a conjecture. Future Work . Proof of M rule admissibility in GL/2 , or a completeness proof (using cut elimination). . Evaluation of the implementation against alternative proof search systems for L/ukasiewicz logics. . Elimination of EC and S rules, and comparison of that system with Metcalfe's GL/ l labelled sequent calculus [4]. . Merge Compaction on other sequent and hypersequent systems where the M rule is admissible (such as RM). . Apply a similar technique for other kinds of shuffling rules in other sequent and hypersequent systems. Reference
Average Merge Time An Intuitive Interpretation
No full textAbstractA classical result by Nagler ([1]) states that the average comparison time of the standard merge algorithm over merge pairs of size n, i.e. pairs of sorted lists of size n, is:
T
M(n,n) =
2n2n+1
Nagler's proof sheds little light on the intuition which should be given to this quotient. We provide an intuitive interpretation of Nagler's result, based on the notion of an “alternation”. This notion essentially represents a switch between the two lists in mergings “pop-the-top” process.We recall from ([2]) that the average number of alternations over all possible merge pairs of size n is n and provide a simplified proof of this fact. We show that the set of all merge pairs of size n which have exactly the average number of alternations turns out to be a “representative” of the entire set of merge pairs in the following sense: the average comparison time over all merge pairs, i.e.
T
M(n,n)
, is identical to the time
T
M[(n,n)]
which merge takes when restricted to merge pairs which have exactly n alternations merge pairs which have exactly the average number of alternations.Finally, we show that the average time,
T
M[n,n] =
2n2n+1
, can be expressed intuitively as the product of the average number of alternations by the average number of comparisons that occur between alternations
Human factors in the moving merge process
No full textThe objective of this research was to investigate the importance of some independent environmental variables in the moving merge process utilizing human factors as indices of driver comfort in such a dynamic situation. The stress producing situation chosen for the evaluation of the sensation of "well being" in an automobile driver was the routine urban task of entering a freeway. A technique of this type could serve as a design tool for the traffic engineer in the modification of present facilities and the design of future roadways. Six variables were investigated as to their importance in a moving merge operation. They were 1) ramp approach speed, 2) velocity on the freeway, 3) backsight available to the driver, 4) advice to the ramp driver as to the velocity on the freeway, 5) posted speed limits on frontage road and freeway and 6) the driver. An experimental design involving these elements was composed using the random balance design technique. The formulated design was such that each factor could be studied individually in order to be able to evaluate the capability of each index to differentiate between conditional levels of the variables. ..
Reusable modelling and simulation of flexible manufacturing for next generation semiconductor manufacturing facilities
No full textAutomated material handling systems (AMHS) in 300 mm semiconductor manufacturing facilities may need to evolve faster than expected considering the high performance demands on these facilities. Reusable simulation models are needed to cope with the demands of this dynamic environment and to deliver answers to the industry much faster. One vision for intrabay AMHS is to link a small group of intrabay AMHS systems, within a full manufacturing facility, together using what is called a Merge/Diverge link. This promises better operational performance of the AMHS when compared to operating two dedicated AMHS systems, one for interbay transport and the other for intrabay handling. A generic tool for modelling and simulation of an intrabay AMHS (GTIA-M&S) is built, which utilises a library of different blocks representing the different components of any intrabay material handling system. GTIA-M&S provides a means for rapid building and analysis of an intrabay AMHS under different operating conditions. The ease of use of the tool means that inexpert users have the ability to generate good models. Models developed by the tool can be executed with the merge/diverge capability enabled or disabled to provide comparable solutions to production demands and to compare these two different configurations of intrabay AMHS using a single simulation model. Finally, results from simulation experiments on a model developed using the tool were very informative in that they include useful decision making data, which can now be used to further enhance and update the design and operational characteristics of the intrabay AMHS
Merge SOM for temporal data
No full textStrickert M, Hammer B. Merge SOM for temporal data. Neurocomputing. 2005;64:39-71
Multiresolution-based image fusion with additive wavelet decomposition
No full textThe standard data fusion methods may not be satisfactory to merge a high-resolution panchromatic image and a low-resolution multispectral image because they can distort the spectral characteristics of the multispectral data. The authors developed a technique, based on multiresolution wavelet decomposition, for the merging and data fusion of such images. The method presented consists of adding the wavelet coefficients of the high-resolution image to the multispectral (low-resolution) data. They have studied several possibilities concluding that the method which produces the best results consists in adding the high order coefficients of the wavelet transform of the panchromatic image to the intensity component (defined as L=(R+G+B)/3) of the multispectral image. The method is, thus, an improvement on standard intensity-hue-saturation (IHS or LHS) mergers. They used the ¿a trous¿ algorithm which allows the use of a dyadic wavelet to merge nondyadic data in a simple and efficient scheme. They used the method to merge SPOT and LANDSATTM images. The technique presented is clearly better than the IHS and LHS mergers in preserving both spectral and spatial information
Observational refinement and merge for disjunctive MTSs
No full textModal Transition System (MTS) is a well studied formal-
ism for partial model specification. It allows a modeller to distinguish
between required, prohibited and possible transitions. Disjunctive MTS
(DMTS) is an extension of MTS that has been getting attention in re-
cent years. A key concept for (D)MTS is
refinement
, supporting a devel-
opment process where abstract specifications are gradually refined into
more concrete ones. Refinement comes in different flavours:
strong
,
ob-
servational
(where
τ
-labelled transitions are taken into account), and
alphabet
(allowing the comparison of models defined on different alpha-
bets). Another important operation on (D)MTS is that of
merge
: given
two models
M
and
N
, their merge is a model
P
which refines both
M
and
N
, and which is the least refined one.
In this paper, we fill several missing parts in the theory of DMTS refine-
ment and merge. First and foremost, we define observational refinement
for DMTS. While an elementary concept, such a definition is missing
from the literature to the best of our knowledge. We prove that our defi-
nition is sound and that it complies with all relevant definitions from the
literature. Based on the new observational refinement for DMTS, we ex-
amine the question of DMTS merge, which was defined so far for strong
refinement only. We show that observational merge can be achieved as a
natural extension of the existing algorithm for strong merge of DMTS.
For alphabet merge however, the situation is different. We prove that
DMTSs do not have a merge under alphabet refinement
Perfectly load-balanced, optimal, stable, parallel merge
No full textWe present a simple, work-optimal and synchronization-free solution to the problem of stably merging in parallel two given, ordered arrays of m and n elements into an ordered array of m+n elements. The main contribution is a new, simple, fast and direct algorithm that determines, for any prefix of the stably merged output sequence, the exact prefixes of each of the two input sequences needed to produce this output prefix. More precisely, for any given index (rank) in the resulting, but not yet constructed output array representing an output prefix, the algorithm computes the indices (co-ranks) in each of the two input arrays representing the required input prefixes without having to merge the input arrays. The co-ranking algorithm takes O(log min(m,n)) time steps. The algorithm is used to devise a perfectly load-balanced, stable, parallel merge algorithm where each of p processing elements has exactly the same number of input elements to merge. Compared to other approaches to the parallel merge problem, our algorithm is considerably simpler and can be faster up to a factor of two. Compared to previous algorithms for solving the co-ranking problem, the algorithm given here is direct and maintains stability in the presence of repeated elements at no extra space or time cost. When the number of processing elements p does not exceed (m+n)/log min(m,n), the parallel merge algorithm has optimal speedup. It is easy to implement on both shared and distributed memory parallel systems
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