Download Advances in Electronic Commerce, Web Application and by Yan-qing Chen, Jian-min Qi, Ling-juan Shi (auth.), David PDF

By Yan-qing Chen, Jian-min Qi, Ling-juan Shi (auth.), David Jin, Sally Lin (eds.)

ECWAC2012 is an built-in convention dedicated to digital trade, internet software and conversation. within the this court cases you will find the rigorously reviewed medical consequence of the second one overseas convention on digital trade, net software and communique (ECWAC 2012) held at March 17-18,2012 in Wuhan, China, bringing jointly researchers from all over the global within the field.

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Finally, a numerical example is provided to illustrate the validity of our results. 2 Problem Statement and Preliminaries Consider the following delay systems with Markovian jump parameters: ⎧⎪ x ( t ) = A ( rt ) x ( t ) + B ( rt ) x ( t − τ ( t ) ) ⎨ ⎪⎩ x ( t0 + θ ) = ϕ (θ ) , ∀θ ∈ [ −τ , 0] (1) where x ( t ) ∈ R n is the state vector , τ ( t ) > 0 is time-varying delay which satisfies 0 ≤ τ ( t ) ≤ τ ,τ ( t ) ≤ τ d < 1 , and ϕ (θ ) is the initial condition function. {rt } , t ≥ 0 is a right- continuous Markov process on the probability space taking values in a finite state space, ℘ = {1, 2," , N } with generator Π = ( λi , j ) , i, j ∈℘given by ⎧⎪λij Δ + ο ( Δ ) , j ≠ i Pr {rt +Δ = j rt = i} = ⎨ , ⎪⎩1 + λij Δ + ο ( Δ ) , j = i where Δ > 0 , lim Δ→ 0 ο (Δ) Δ = 0 , λij ≥ 0 for j ≠ i is the transition rate from mode i at time N to mode j at time t + Δ, λii = −∑ λij .

5 34 X. Li and K. Da 5 Summaries Since evaluation index are at different levels in multi-index evaluation system, they have different effecting on evaluation, even though they are at the same level, every attribute may have difference in significance. Therefore, it is important to determine the weight coefficient in fuzzy comprehensive evaluation, which will affect the evaluation result directly. By the analysis in this paper, we use fuzzy comprehensive evaluation based on RST to reduce attributes of core competency for human capital force indexes.

In Table 1, we illustrated some multi-modal recognition fusion results. The first column denotes the key frame of a video shot. The second column lists the annotation of our system. Table 1. Video shot annotation result Shot image annotation ─ ─ ─ ─ Shot image annotation Tree Environment Child Grass Spring ─ ─ ─ ─ ─ Swim Woman Track Champion Record ─ Train Railway Hill Success Plan ─ ─ ─ ─ ─ ─ Climate Weather Sea Wind Shore Forecast ─ ─ ─ ─ Cloud Sea Weather Program ─ Summer ─ Tree ─ Dew ─ Grass ─ ─ ─ ─ ─ A Foreground Segmentation Scheme 27 4 Conclusion We have presented a semantic mining method which exploits the three aspects of modals in videos.

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