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Acknowledgements Supported by the Natural Science Foundation of Fujian Province of China under Grant Nos.
F0910149, S0850028; the Soft Science Research of Fujian Province of China under Grant No. 2010R0091; the Key Technology R&D Program of Xiamen, Fujian under Grant No. 3502Z20103001, 3502Z20101002; the Key Technology R&D Program of Quanzhou, Fujian under Grant No. 2009G29; the Leading Academic Discipline Program, “Project 211 (the 3rd phase)” of Xiamen University; the Fundamental Research Funds for the Central Universities, Xiamen University under Grant No. 2011121023, CXB2012012, CXB2012013, 201212G007.
[9] Shi Hong-Bo, Wang Zhi-Hai, Huang Hou-Kuan, Li Xiao-Jian.Journal of Software, 2004.193~199,in Chinese
[10] Zhu, Xiaodan; Su, Jinsong; Wu, Qingfeng; Dong, Huailin.Naive Bayes classification algorithm based on optimized training data.Advanced Materials Research,2012.v490-495,p460-464

The material of the brake element is 16MnR steel, whose Young's modulus and Poisson ratio are 2.1e5 MPa and 0.3 respectively.
Reasonable design and appropriate friction pair material can reduce vibration amplitude and avoid coupling vibration of brake element and bogie.
Journal of Traffic and Transportation Engineering. (2005).5:3, p. 5-7.
China Railway Science, (1985)6:1, p99-110.
China Railway Science, 2005, 26(6): 6-12 (in Chinese)

Acknowledgments This project was supported by National Natural Science Fund of China (No.6047804) and Key Project of Tianjin Science and Technology Committee (No.033188011).
Guo: Materials and Manufacturing Processes, Vol. 13 (1998) No.4, pp. 537-554
Steen: Journal of laser application, Vol. 12 (2000) No.1, pp. 28-33

Cutter Optimal Selection of CAPP System Facing to the Modern Manufacture Lan Li1,a, Enfu Liu 1 and Qingsu Jin1 1 College of Mechanical and Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei 050054, China a lanlan3009@126.com Keywords: Modern manufacture, Cutter selection, Fuzzy integrate, Fuzzy assessment, Fuzzy selection Abstract.
Therefore, three main influence factors of following four aspects are chosen as evaluation factors: 1) Processing quality aspect: dimensional precision, form and positional precision, surface quality. 2) economy aspect: processing cost., cutting time, technical contents. 3) Resources and energy sources aspect: energy duty factor, equipments duty factor, material duty factor. 4) Environmental protection aspect: the noise pollution, the wastes pollution, the oil fog pollution.
Table 1 Two grades fuzzy assessment to alternative cutter The unilateral fuzzy synthetic assessment Synthetic assessment Serial number Assessment aspect Serial number Ι 1 2 3 Primary assessment Weight value Result Evaluation factors Dimensional precision Form and positional precision Surface quality Important degree 0.9 0.9 0.7 HioMi XDLY06 0.8 0.7 0.8 0.8 XDLY07 0.6 0.6 0.6 0.6 XDLZC11 0.7 0.5 0.4 0.7 1 Processing quality Assessment values Hi XDLZC12 0.4 0.3 0.3 0.4 m1 1.0 HaoM 0.8 Evaluation factors Processing cost Cutting time Technical contents Important degree 0.9 0.8 0.7 HioMi XDLY06 0.6 0.8 0.8 0.8 XDLY07 0.7 0.7 0.6 0.7 XDLZC11 0.7 0.6 0.5 0.7 2 Economy Assessment values Hi XDLZC12 0.6 0.5 0.3 0.6 m2 0.9 HboM 0.7 Evaluation factors Energy duty factor Equipments duty factor Material duty factor Important degree 0.7 0.6 0.5 HioMi XDLY06 0.8 0.7 0.6 0.7
Wu: Journal of Qing Hua University (Natural Science), (1998) No.2.

Acknowledgement This work was supported by National Natural Science Foundation for Young Scholars of China (Grant No. 51104188).
Chiriac, Materials Science and Engineering B, v 152, 2008, p 81-85 [3] H.
Lu, Journal of Alloys and Compounds, 479 (2009) 78

Application of Intelligent Algorithm to Transformer Optimal Design Shuang ZHANG 1, a, Qinghe HU 2,b, Xingwei WANG 3,c 1Software College, Northeastern University, 110004 2,3Information Science and Engineering College, Northeastern University, 110004 azhangs914@163.com, bhuqinghe@ise.neu.edu.cn, cwangxingwei@ise.neu.edu.cn Keywords: Transformer, Optimal design, Genetic algorithm, Total owning cost Abstract.
With the harder marketing competition, increasing raw material price, higher requirement for energy saving and lower energy consumption, transformer designer is facing problems of how to choose parameters to meet both technical requirement and to reduce cost and loss.
Constraints include self, performance, material and process constraints.
Acknowledgment This work is supported by the National Natural Science Foundation of China (Grant No. 60673159, 70671020, 60802023).
Optimizing production decisions using a hybrid simulation-genetic algorithm approach [J], Canadian Journal of Agricultural Economics 2009, 57(1): 35-54

Basal Heave Stability Analysis of Deep Foundation Pit in Soft Soil Desen Kong1, 2, a, Yanqing Men1, b 1College of Civil Engineering and Architecture, Shandong University of Science and Technology, Qingdao, 266590, China 2Shandong Provincial Key Laboratory of Civil Engineering Disaster Prevention and Mitigation, Shandong University of Science and Technology, Qingdao, 266590, China adskong828@163.com, btengwei1234@126.com Keywords: Basal heave stability, Foundation pit, Soft soil, Numerical analysis Abstract.
The bracing length was only half of the excavation width responding to the restraining action from upright piles over there. 8-node reduced-integration elements were applied in both of the soil and support structure materials, with the total elements and total nodes were 4812 and 15126 respectively.
Bolton, Ground movement predictions for braced excavations in undrained clay, Journal of Geotechnical and Geoenvironmental Engineering. 132 (2006) 465-473

Taking into account the effect of different glass content of their basic mechanical properties, this paper tests carried out concrete cube compressive strength and axial compressive strength experiment using waste glass aggregate concrete[4]. 2.Experiment material The glass used in the experiment is the rest of producing glass from a glass factory in Shenyang City.
Table 2 The mixture ratio of each specimen Number The ratio of glass replacing sand (%) Amount of material per cubic meter of concrete（kg） Quantity cement stone glass sand water 1 0 453 1191 — 560 195 6 2 50 453 1191 264 281 195 6 3 100 453 1191 525 — 195 6 4.
Acknowledgements The authors wish to thank the Planned Science and Technology Project of Shenyang, China (F12-173-9-00) and Science and technology program of Liaoning Province (2011222007) for sponsoring this research project.
[4]Xia B H, Liu Y C, Xu G L, Study on Compressive Properties of Waste Glass, Concrete Journals,2013

Experimental Sections 2.1 Materials: Purified brine (Sulfate-rich brine from Tianjin Tanggu Saltworks, SO42- concentration is 0.136mol/L) and calcium chloride solution (reagent grade, Tianjin Guangfu fine chemical research institute, Ca2+ concentration is 0.137mol/L).
Acknowledgements This work was financially supported by “Public science and technology research funds projects of ocean (201005021) and (201405008)”.
Journal of Membrane Science, 252(2005) 253-263

Experimental Part Characterization of Raw Materials.
Acknowledgements The work was supported by Guiyang Municipal Science and technology program [2012205]6-4 References [1] M Ahmaruzzaman: Progress in Energy and Combustion Science, Vol. 36 (2010) No.3, p.327
(in Chinese) [7] N R Yang: Journal of Chinese Ceramics Society, Vol. 24 (1996) No.2, p.209.