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machining time, machining quality, machining cost and other especial machining tools or fixtures[6]. Because LMU includes eight elements, and each element has much different information, through combining different information of each element, many LMU-s will be produced and LMU model database will be set up. Thus, when designing the LMP, designers only choose the appropriate LMU-s according to the manufacturing process of a complex part from LMU model database, in which each LMU has been designed and defined, so the LMU information of LMP is relatively static[7]; after choosing the appropriate LMU, designers should evaluate and compute the machining time, machining quality, machining cost and point out the especial machining tools or fixtures for this LMU, and these information are dynamic, because they are designed according to this part[8]. The design of LMP is shown in Fig.1. part family geometric feature CAD information of part CAPP information of part LMU(i) material type rough type Time requirement of the LMU LMU model database dimension range machining method Design LMU Design LMP i from 0 to N-1 Cost requirement of the LMU Equipment demands Total restriction and weight of time precision grade production type Manufacturing process knowledge database Order form information of part Quality requirement of the LMU Fixture demands Tool demands complex part, and is the mapping result between PMU and networked manufacturing sub-tasks (LMU-s). EMP is corresponding to LMP. It is a sequence of several PMU-s, and includes the detail manufacturing resources information of PMU-s. The mapping process is shown in Fig.2. The 1 st LMU dynamic information The 1 st PMU Detail resource information The 2 st LMU dynamic information The 2 st PMU Detail resource information LMP EMP The n st LMU dynamic information Subtask——Manufaturing unit The n st PMU Detail resource information Networked manufacturing task Mapping Networked manufacturing resource Fig.2 The mapping process of logical manufacturing tasks and For example shown in Fig.3, if a physical manufacturing unit marked as PMU1 accepted a manufacturing order of a complicated part, after analyzing the process of this part, process planners decompose the whole task to N+1 sub-task in considering itself resources. Among these sub-tasks, two sub-tasks marked as LMU (0) and LMU (N) can be completed by PMU1. For other manufacturing sub-tasks, the cooperative PMU-s should be deployed in considering the total running time, cost and the total manufacturing quality. LMU(0) LMU(1) LMU(2) LMU(N-1) LMU(N) Total restriction and weight of cost Total restriction and weight of quality PMU1 PMU3 PMU2 PMU3 PMU1 Fig.1 Designing flow of LMP PMU4 PMU4 PMU4 C. Information model of networked manufacturing task So, for a complex part, which has N manufacturing sub-tasks, the information model of the whole manufacturing task is described by the following formula: LMP= { LMU(0), C , T , Q , F, E ; LMU(1), C, T, Q, F, E; ⋅⋅⋅⋅; 0 0 0 0 0 1 1 1 1 1 ⑴ LMUN ( −1), C , T , Q , F , E } N−1 N−1 N−1 N−1 N−1 Where: C i, Ti , Qi are the cost, time and quality restrictions for finishing the LMU (i) ; E i and F i are the requirements for especial equipments and fixtures; i is an integer, i ∈[ 0, N −1] . Ⅲ. FORMULATION FOR COMPLICATED PARTS A. Problem description Definition 4: Executive manufacturing process (EMP) expresses the whole practical manufacturing process of a PMU5 PMU6 PMU6 PMU5 Fig.3 The problem description of networked manufacturing resources optimization deployment B. Mathematics model 1) Selection fields of PMU and EMP For a complicated part, after designing the LMP, according to the detail information of each LMU, PMU-s that can complete this LMU will be selected out and these PMU-s are called as PMU subset. The mapping result for the i -the subtask— LMU (i) in LMP is marked as: G { PMU( i,0) , PMU( i,1 ), ⋅⋅⋅, PMU( i, m −1 )} ⑵ i = i 202
Where: G i is the selection field of PMU corresponding to LMU (i) ; m i is the number of PMU in G i ; i is an integer and ranges from 0 to N-1; N is the number of sub-tasks, namely the quantity of LMU in LMP. So, for the whole networked manufacturing task— LMP described in ⑴, all PMU subsets can be obtained and described as: G = { G0, G1, ⋅⋅⋅, GN −1} ⑶ This also is the selection fields of EMP, 2) Objective functions For the whole networked manufacturing tasks formulated by ⑴, EMP is orderly composed of N PMU-s, marked as: EMP = { PMU (0, k ), PMU (1, k ), ⋅ ⋅ ⋅, 0 1 ⑷ PMU ( N − 1, k N )} −1 Where: k i ∈[ 0, mi −1] ; PMU i, k ) is the k i -th PMU in G i ; i ( i m is the number of PMU in G i . In order to obtain the optimal EMP, each EMP should be evaluated in terms of some qualitative or quantitative criterions. In this paper, the following three criterions will be considered: the least total running cost—C, the least total running time—T and the best total machining quality — Q. Thus, the objectives functions are formulated by ⑸. ⎧ minC ⎪ = min( C + C N −1 N − 2 ) = min( ∑ C i ( j) + ∑ C i = 0 i = 0 in tr ⎪ ⎪ ⎨minT = min( T N −1 N − 2 + T )= min( ∑ T ( j) + ∑ T tr i in i ⎪ i = 0 i = 0 ⎪ ⎪ ⎪ minQ ⎩ N −1 = min ∑ (1 - Q i ( j)) i = 0 i, , i i + 1 + 1 ( j, k )) ( j, k )) Where: PMU ( i, j) is the j -th in G i , j ∈ [ 0, mi −1], k ∈[0, m i + 1 −1] , i ∈[ 0, N −1] m i is the number of PMU in G i , C i ( j) is the machining cost inside PMU ( i, j) , C i , i + 1 ( j , k ) is the transportation cost between PMU ( i, j) in G i and PMU ( i + 1, k) in G i + 1 , T i ( j) is the machining time inside PMU ( i, j) , T i , i+ 1( j, k) is the transportation time between PMU ( i, j) in G i and PMU ( i + 1, k) in G i+1 , Qi ( j) is the machining quality, which is guaranteed by PMU ( i, j) , expressed by the rate of certified parts quantity divided by all parts quantity. 3) Restriction conditions For LMP, R and D are assumed as the released time and the delivery time. C T and Q T are respectively assumed as the total cost restriction and the total quality restriction. During [R, D], all LMU-s in LMP must be completed. Similarly, LMU (i) has a released time R i and a delivery time D i , and has the cost, time and quality restrictions expressed by C i , T i and Q i . During [R i , D i ], all work procedures of LMU (i) must be completed. Thus, the restriction conditions of networked manufacturing resources optimization deployment for complicated part include: ⑸ The sequence restriction of sub-tasks:if LMU(i) must be completed before LMU(j), then i i i j R + T ≤ D ≤ R . ⑹ The running time restrictions of LMU (i): i i ⎪ ⎧maxT ( j) ≤ T ≤ ( D i N −1 N −2 ⎨ max( ∑T ( j) + ∑T i i, ⎪⎩ i= 0 i= 0 i − R ) ( j, k)) ≤ ( R − D) The running cost restrictions of LMU (i): i ⎪ ⎧maxCi ( j) ≤ C N −1 N −2 ⎨ max( ∑Ci ( j) + ∑Ci ⎪⎩ i= 0 i= 0 i+ 1 , i+ 1 ( j, k)) ≤ C The total quality restrictions of LMU (i): N ⎪ ⎧ ∑ − T min 1 Q ( j) / N ≥ Q i ⎨ i= i ⎪⎩ minQ ( j) ≥ Q i T ⑺ ⑻ 0 ⑼ Ⅳ. OPTIMIZING DEPLOYMENT ALGORITHMS BASED ON GENETIC ALGORITHM GA is widely applied in advanced manufacturing fields, such as the optimum scheduling of workshop resources, the partner selection of virtual enterprise. In this paper, for the convenience and speediness of computation, we transform the multi-objectives optimization problem formulated by ⑸ into a single objective optimization problem to solve with GA. Exterior information is not almost used when searching the best solution by GA, and only the value of fitness function is taken into consideration. In order to make the optimization result impersonal and precise, we introduce the relative membership degree to the best member of fuzzy mathematics into the designing of relative fitness function. A. Coding method According to the characteristics of networked manufacturing resources optimization deployment, the integer coding method is applied in GA. The coding regulations include: • Coding the manufacturing subtask: the code is 0,1,…,N-1 (N is the number of subtasks, namely the number of LMU in LMP). • A gene of a chromosome corresponds to a LMU in LMP, and the length of a chromosome, namely the quantity of genes in a chromosome, is equal to the quantity of LMU in LMP. • Coding the manufacturing resources: the code is 0, 1,…, m −1, where m i i is the quantity of PMU in G i . • A gene value of a chromosome corresponds to a manufacturing resource, namely a PMU. • x i (the i -th gene value of a chromosome) expresses the xi -th PMU in G i , and the corresponding manufacturing subtask is the i -th LMU in LMP. x is integer and 0 ≤ x p m . i i • A chromosome corresponds to an EMP. For the chromosome expressed by ⑽, the corresponding EMP expressed by ⑾. X [ x x ⋅⋅⋅ x ⋅⋅⋅ ] i x ⑽ 1 = 0 1 N − i 203
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Proceedings The Second Internationa
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Table of Contents Message from the
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Zuming Xiao, Zhan Guo, Bin Tan, and
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Message from the Symposium Chairs T
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Second International Symposium on N
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ISBN 978-952-5726-09-1 (Print) Proc
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model that can deal with time serie
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training for the Wushu competition
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information, called weak uncertain
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Student side Student side Student s
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If we define element of student as
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QoS, each frame data is divided int
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for Imaging Two and Three Phase Flo
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A. Profiling&following control algo
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Research and Realization about Conv
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PDF document structure is a tree st
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support 10 Mb / s. But ENC28J60 onl
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condition the first byte output of
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According to maximum membership deg
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ubber according to the mass ratio o
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Ⅲ. AN IMPROVED DNA ALGORITHM FOR
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Ⅴ.CONCLUSION REMARKS DNA computer
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its first child q 1 on the left, th
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chains can greatly improve efficien
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II. RELATED WORK A. Mobile Service
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special services. SOAP is used to b
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detection methods of DDoS attacks m
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Step4 calculate the new subordinate
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Figure 1. An analysis of the partit
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The preceding three formulas can be
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And the decay speed of buffer seque
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distance of view point. Given that
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Ⅳ. EVALUATION OF BLENDED LEARNING
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symmetric with respect to the origi
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each sample belongs to each categor
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A. Data Preparing To generate our t
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oth α and β . determination of Qu
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Strong earthquake 0.1< M L
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indirect causes, and the logical re
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egression. Granger causality test i
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B. Evaluation We have evaluated thi
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used as a source and neighboring ce
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Figure 3. Drainage networks generat
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Combinational logic unit failures i
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Tabal.1 Combinational fault logic t
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under the endorsement of both the m
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TABLE I. DESCRIPTION OF PROPOSITION
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Figure 2. The process of the invers
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Figure 9. (a)the original image.(b)
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In order to reduce to the number of
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that studying being going to be to
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Reference[10] analyzed the evolutio
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module, communication module, apper
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After the comprehensive performance
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system testing can be seen that the
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III. AUTONOMIC RESOURCE ALLOCATION
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The QoE i (T i ) is the ith user’
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Page 165 and 166: Where n is the number of data point
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Page 173 and 174: SQL Azure will eventually include a
Page 177 and 178: The nodes in the suffix tree are dr
Page 179 and 180: [3] Y. Li, S. M. Chung, and J. D. H
Page 181 and 182: some drilling fluid produces hydrog
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Page 227 and 228: Ⅵ. CONCLUSION Figure 6. The Flask
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The reduction process consists of t
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all eight normal vectors are identi
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is B object , and the number of emp
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xi, j y' = εα ( (1 + tanh( )) −
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Error 8000 7000 6000 5000 4000 3000
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Architecture (AMBA) a new bus archi
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In order to ensure the smooth proce
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In this paper, we adopt two Sobel o
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four layers.The far right of the gr
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TABLE II. OPERATIONAL EMPLOYEE TABL
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A. Object-relational Type To audit
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to execution time. Also, DBA would
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Liping Chen .......................
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