http://www.sensorsportal.com/HTML/DIGEST/Journal_Subscription.htm SSeennssoorrss && TTrraannssdduucceerrss Volume 16, Special Issue November 2012 www.sensorsportal.com ISSN 1726-5479 Guest Editors: Gongfa Li, Tinggui Chen and Hegen Xiong Editors for Western Europe Meijer, Gerard C.M., Delft University of Technology, The Netherlands Ferrari, Vittorio, Universitá di Brescia, Italy Editors for North America Datskos, Panos G., Oak Ridge National Laboratory, USA Fabien, J. 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Clive, Louisiana State University, USA Wu, DerHo, National Pingtung Univ. of Science and Technology, Taiwan Wu, Zhaoyang, Hunan University, China Xiu Tao, Ge, Chuzhou University, China Xu, Lisheng, The Chinese University of Hong Kong, Hong Kong Xu, Sen, Drexel University, USA Xu, Tao, University of California, Irvine, USA Yang, Dongfang, National Research Council, Canada Yang, Shuang-Hua, Loughborough University, UK Yang, Wuqiang, The University of Manchester, UK Yang, Xiaoling, University of Georgia, Athens, GA, USA Yaping Dan, Harvard University, USA Ymeti, Aurel, University of Twente, Netherland Yong Zhao, Northeastern University, China Yu, Haihu, Wuhan University of Technology, China Yuan, Yong, Massey University, New Zealand Yufera Garcia, Alberto, Seville University, Spain Zakaria, Zulkarnay, University Malaysia Perlis, Malaysia Zagnoni, Michele, University of Southampton, UK Zamani, Cyrus, Universitat de Barcelona, Spain Zeni, Luigi, Second University of Naples, Italy Zhang, Minglong, Shanghai University, China Zhang, Qintao, University of California at Berkeley, USA Zhang, Weiping, Shanghai Jiao Tong University, China Zhang, Wenming, Shanghai Jiao Tong University, China Zhang, Xueji, World Precision Instruments, Inc., USA Zhong, Haoxiang, Henan Normal University, China Zhu, Qing, Fujifilm Dimatix, Inc., USA Zorzano, Luis, Universidad de La Rioja, Spain Zourob, Mohammed, University of Cambridge, UK Sensors & Transducers Journal (ISSN 1726-5479) is a peer review international journal published monthly online by International Frequency Sensor Association (IFSA). Available in electronic and on CD. Copyright © 2012 by International Frequency Sensor Association. All rights reserved. SSeennssoorrss && TTrraannssdduucceerrss JJoouurrnnaall CCoonntteennttss Volume 16 Special Issue November 2012 www.sensorsportal.com ISSN 1726-5479 Research Articles Foreword Gongfa Li, Tinggui Chen, Hegen Xiong.............................................................................................. I Research of a Novel Attitude Regulating System of Autonomous Underwater Glider Rong Liu, Jie Yu ................................................................................................................................. 1 Study on the Vibration of the 1700 Four Rollers Rolling Mill Zehao Wu, Gongfa Li, Po Gao, Yuan He, Dawei Tan........................................................................ 11 The Design of Wireless Sensor Application System Based on Internet of Things Zhang Ye, Zhang Feng, You Fei ........................................................................................................ 18 Design and Simulation of the Air Compressor Control System Yuan He, Gongfa Li, Po Gao, Zehao WU and Cunyuan Li ................................................................ 27 Precise Time Synchronization Technology for Distributed Automated Test System Yan Xu, Ming Li, and Jiangtao Dong.................................................................................................. 35 FEM-based Simulation of Stress Distribution in U71Mn Heavy Rail during Quenching Siqiang Xu, Jianyi Kong, Gongfa Li, Jintang Yang, Hegen Xiong and Guozhang Jiang ................... 43 LTS: An Improved Self-Adaptive Data Replication Strategy for File Sharing in Grid Liang Hu, Jia Zhao, Xiaodong Fu, Lin Lin, XJianfeng Chu ................................................................ 52 Influence of Expansion Joint of Bottom Lining in Ladle Composite Construction Body on Thermal Stress Guozhang Jiang, Gongfa Li, Jianyi Kong, Liangxi Xie and Siqiang Xu.............................................. 61 Numerical Simulation of the Gap Flow Field for CEDMMG Compound Machining Engineering Ceramics Renjie Ji, Yonghong Liu, Chao Zheng, Fei Wang, Yanzhen Zhang, Yang Shen, Baoping Cai......... 68 Coke Oven Heating Model of Fuzzy Intelligent Control System Guozhang Jiang, Gongfa Li, Jianyi Kong and Liangxi Xie ................................................................. 75 Three-Dimensional Model and Test Analysis of the 60t Converter Po Gao, Gongfa Li, Zehao Wu, Yuan He, Ke Zhang ......................................................................... 84 A Multi-pipe and Multi-address Switching Wireless Network for Distributed Seismic Data Acquisition System Li Huailiang, Tuo Xianguo, Zhu Lili..................................................................................................... 92 Remote Monitoring and Diagnosis System of Rolling Mill Transmission System Gongfa Li, Yuesheng Gu, Zehao Wu, Han Xiao, Guozhang Jiang, Jianyi Kong, Liangxi Xie and Siqiang Xu .................................................................................................................................. 101 http://www.sensorsportal.com Study on Frequency Division Technique of Photoelectric Encoder Shi-xiong Zhang ................................................................................................................................. 108 Job-shop Scheduling Problem Based on Particle Swarm Optimization Algorithm Ying Sun and Hegen Xiong ................................................................................................................ 116 Wireless Sensor Network Data Fusion Algorithm Based on Neural Network in the Area of Agriculture Yi Zhou, Gelian Song, Maohua Wang................................................................................................ 128 Optimal Design of Gear Based on Quantum Genetic Algorithm Ying Sun and He-gen Xiong............................................................................................................... 137 Independent Axiom-based Robust Design for Nonlinear System Xianfu Cheng...................................................................................................................................... 144 Development of a New Kind of Multihole Cylindrical Electrode Used for Electrical Discharge Milling Fei Wang, Yonghong liu, Renjie Ji, Yanzhen Zhang, Zemin Tang, Xiangzhen Han.......................... 152 Energy Efficiency Evaluation of Iron and Steel Enterprise Guozhang Jiang, Gongfa Li, Jianyi Kong and Liangxi Xie ................................................................. 161 Classification Model of Traffic Emergency Response to Urban Emergency Yaqin He, Jie Li .................................................................................................................................. 169 Modeling the Machining Parameters in End Electric Discharge Milling of Silicon Carbide Ceramic Renjie Ji, Yonghong Liu, Yanzhen Zhang, Fei Wang, Chao Zheng, Yang Shen............................... 179 Testability Analysis of Inertial Measure Unit Based on Multi-signal Model Xing He, Hongli Wang, Jinghui Lu...................................................................................................... 188 Vibration Model of Pipe Conveying Fluid Considered Fluid Structure Interaction Gongfa Li, Yuesheng Gu, Zehao Wu, Han Xiao, Jianyi Kong, Guozhang Jiang, Liangxi Xie, Li Hu and Siqiang Xu ................................................................................................................................... 197 Optimization SVM Algorithm and it’s Application in Agricultural Science and Technology Project Classification Hui Feng Yan, Wei Feng Wang, Qin Mao, Ming Liang Zhou ............................................................. 203 Accuracy of Experimental Platform of Controlled Five-bar Linkages Li Pan.................................................................................................................................................. 210 Analysis on Sliding Velocity of Cycloid Gear Pair during Meshing Shuyan. Wang, Guizhong Tian, Xujun Jiang...................................................................................... 218 Intelligent Diagnosis of Coke Oven Heating Production Gongfa Li, Yuesheng Gu, Jianyi Kong, Guozhang Jiang and Liangxi Xie ......................................... 226 Adaptive Fuzzy Control of Aircraft ABS Based on Runway Identification Hua-wei Wu, Te-fang Chen, Wei-ming Huang................................................................................... 233 The Total Factor Productivity Growth of the Municipal Water Industry in China Wang Fen ........................................................................................................................................... 243 Research on Attainable Moment Subset of Singular Over-actuated System Shi Jingping, Qu Xiaobo..................................................................................................................... 252 Finite Element Analysis of Vane Seals Guozhang Jiang, Li Zhao, Jianyi Kong, Gongfa Li, Liangxi Xie ......................................................... 261 269 Application of Multi-factor Fuzzy Comprehensive Evaluation in Stability of Surrounding Rock of Underground Engineering Jinglong Li, Jiachun Li, Shuchen Li .................................................................................................... Jackknife Control on Tractor Semi-trailer during High Speed Curve Driving Shuwen Zhou and Siqi Zhang ............................................................................................................ 277 Simulation and Experiment Research on Deforming Force of Slab Cold Roll-beating Mingshun Yang, Yan Li, Jianming Zheng, Qilong Yuan..................................................................... 285 Research on Organizational Culture and Resources Integration Model Based on MICK-4FI Yan Zhan, Jiansha Lu and Shiyun Li.................................................................................................. 295 Authors are encouraged to submit article in MS Word (doc) and Acrobat (pdf) formats by e-mail: editor@sensorsportal.com Please visit journal’s webpage with preparation instructions: http://www.sensorsportal.com/HTML/DIGEST/Submition.htm International Frequency Sensor Association (IFSA). http://www.sensorsportal.com/HTML/IFSA_Publishing.htm http://www.iaria.org/conferences2013/SENSORDEVICES13.html http://www.iaria.org/conferences2013/SENSORCOMM13.html http://www.iaria.org/conferences2013/CENICS13.html Sensors & Transducers Journal, Vol. 16, Special Issue, November 2012, pp. I-II I SSSeeennnsssooorrrsss &&& TTTrrraaannnsssddduuuccceeerrrsss ISSN 1726-5479 © 2012 by IFSA http://www.sensorsportal.com Intelligent Data Acquisition and Information Process Technologies and Their Applications (Foreword) 1 Gongfa Li, 2 Tinggui Chen, 1 Hegen Xiong 1 Wuhan University of Science and Technology, China 2 Zhejiang Gongshang University, China Received: 11 September 2012 /Accepted: 11 October 2012 /Published: 20 November 2012 Nowadays, intelligent data acquisition and information process technologies are playing a more and more important role in applied sciences and engineering. This field is experiencing rapid development, and in the last decade has resulted in more intelligent, sensitive, and accurate methods of data acquisition and information process in manufacturing, environmental monitoring, medical monitoring systems and laboratory measurement equipment. Advanced methods have been developed to enhance the measurement of information for automation, in-process inspection, quality control, diagnostics, and other processes. By realizing the challenges and opportunities in this domain, we planned to bring the special issue on the “Intelligent Data Acquisition and Information Process Technologies and Their Applications”. The purpose of this current issue is to present a collection of high-quality academic papers that cover recent advances in measurement, automation, architecture, algorithms, modeling and other core issues as well as peripheral applications in the field. This issue comprises 34 papers carefully selected from the reviewed papers which were presented in 2012 International Conference on Mechanical Engineering, Automation and Material Science (MEAMS' 2012), December 22 to 23, 2012, held at Wuhan University of Science and Technology, Wuhan, China and published in the conference proceedings. This special issue presents the extended version of the conference papers. Papers in this special issue address many up-to-date efforts for information sciences and technologies such as measurement, network protocols and modeling, web ontology, evolutionary algorithms and data mining algorithms, information processing technologies, production information systems, etc. We believe that the issues addressed in the papers have large impact on applied sciences and engineering. We are thankful to all the contributors for their high quality submissions and timely revision. We are also thankful to the editors and the publishers of the Sensors & Transducers Journal. Dr., Prof. Gongfa Li, Dr., Associate Prof. Tinggui Chen, Dr., Prof. Hegen Xiong http://www.sensorsportal.com Sensors & Transducers Journal, Vol. 16, Special Issue, November 2012, pp. I-II II Guest Editors Dr. Gongfa Li received the Ph.D. degree in mechanical design and theory from Wuhan University of Science and Technology in China. Currently, he is a professor at Wuhan University of Science and Technology, China. His major research interests include modeling and optimal control of complex industrial process, mechanical engineering. He is invited as a reviewer by the editors of some international journals, such as Environmental Engineering and Management Journal, International Journal of Engineering and Technology, International Journal of Physical Sciences, International Journal of Water Resources and Environmental Engineering, etc. He has published nearly thirty papers in related journals. Dr. Tinggui Chen is an assistant professor of the school of computer and information engineering, Zhejiang Gongshang University, P. R. China. He graduated from the Jianghan University and earned his B.S. degree in Mechanical design. Then, he received the M.S. degree in industrial engineering from the Wuhan University of Science and Technology and his Ph. D degree in engineering management from Huazhong University of Science and Technology. Hegen Xiong received the Ph.D. degree in materials processing engineering from Huazhong University of Science and Technology, China, in 2005.He is a Professor of College of Machinery and Automation, Wuhan University of Science and Technology. Currently, his research interests are manufacturing information, job shop scheduling, flow shop scheduling and computer aided engineering. ___________________ 2012 Copyright ©, International Frequency Sensor Association (IFSA). All rights reserved. (http://www.sensorsportal.com) Sensors & Transducers Journal, Vol. 16, Special Issue, November 2012, pp. 1-10 1 SSSeeennnsssooorrrsss &&& TTTrrraaannnsssddduuuccceeerrrsss ISSN 1726-5479 © 2012 by IFSA http://www.sensorsportal.com Research of a Novel Attitude Regulating System of Autonomous Underwater Glider Rong Liu, Jie Yu Institute of Mechatronic Engineering, Hangzhou Dianzi University, Hangzhou China, 310018 E-mail: liurzju@gmail.com, yujiett0607@163.com Received: 11 September 2012 /Accepted: 11 October 2012 /Published: 20 November 2012 Abstract: The attitude regulating system plays a most important role in the autonomous underwater glider. A new attitude regulating system which is based on shape memory alloy is developed in this paper. Due to the characteristic property of the shape memory alloy, it is used as the driving material to achieve the goal of fast response. By analyzing the performance of the shape memory alloy wire, the dynamic model of attitude regulation system is established. An experiment is executed to verify that the new designed system presents is suitable to underwater glider. Copyright © 2012 IFSA. Keywords: Attitude regulating system, Dynamic model, Fast response. 1. Introduction Autonomous underwater gliders, represent a rapidly-maturing technology with a large cost-saving potential over currently-available ocean sampling techniques, especially for sustained, month at a time, real-time oceanographic measurements [1], as sketched in the Fig. 1(a).The motivation for developing autonomous underwater gliders is essentially economy [2]. Continuous weight changes as well as an attitude change result in a series of up/down glide cycles. The attitude regulating system plays a most important role in the autonomous underwater glider. Traditional attitude regulating system is sketched in the Fig. 1(b), but it needs other structure to help self-lock. Therefore, developing an adaptive gravity regulating system with fast response is the key to a successful autonomous underwater glider. http://www.sensorsportal.com Sensors & Transducers Journal, Vol. 16, Special Issue, November 2012, pp. 1-10 2 Fig. 1 (a). Autonomous underwater glider. Fig. 1 (b). Traditional attitude regulating system. It is known that a phase transformation in the shape memory alloys (SMA) is the major cause of the dramatic change of their physical properties under appropriate thermo-mechanical loadings. Many instructive investigations have been carried out towards a better understanding of the microstructure and phase transformation in ferroelastic materials [3-6]. With the unique mechanical characteristics and shape memory effect (SME), it has been used widely as force and displacement actuators in many fields, because it has the ability to return to some previously defined shape/size when subjected to appropriate thermal [7]. When the environmental temperature is lower than certain temperature, the SMA exhibits shape memory effect, and the environmental temperature higher than certain temperature, it exhibits the superelastic and hysteretic effect. The large recovery stress of 1GPa and large recoverable strain (6-8 %) capability of SMAs make them superior over other smart materials. In recent years, there has been surge in underwater glider research with many based on SMA actuation [8, 9] and other smart material actuators [10]. In the following section, a new kind of attitude regulating system of autonomous underwater glider was designed taking advantage of the superelasticity of SMA in order to overcome the limitations of the existed system. By use of the existed SMA dynamic model, a dynamic model of the proposed system is formed for the better understanding of the attitude regulating system. 2. SMA Attitude Regulating System Fig. 2 is the structure of the attitude regulating system. The structure of using SMA wire to regulating displacement of the slide block is presented. Fig. 2. Structure of the attitude regulating system. 1 - Linear guide rail; 2-L-liked block; 3-Preload screw; 4-Sliding block, 5-Insulated board, 6-Left T-liked block; 7-SMA wire; 8-Right T-liked block; 9-Fixed screw; 10-Spring; 11-U-liked block. Sensors & Transducers Journal, Vol. 16, Special Issue, November 2012, pp. 1-10 3 The system is completely symmetry from the center line. So here I describe the left side of the center line, which is the same as the other side. The system includes a linear guide rail. There are four sliding blocks on the linear guide rail and they can slide on the rail smoothly. Two T-liked blocks are placed on the sliding block and L-liked block is connected with left T-liked block through the preload screw. The SMA wire is fixed between the left T-liked block and right T-liked block through the screw. U-liked block is placed on the center of the rail. The spring is fixed between the right T-liked block and the U-liked block. 3. Working Principle of the Attitude Regulation System The preload screw is screwed to make the SMA wire whose strain is about 0.08 stretch to its best. When the autonomous underwater glider is put into the water, the SMA wire is heated by electrifying. With the increasing of the SMA wire temperature, SMA phase converts from martensite to austenite. The SMA wire wants to restore to its original shape, which therefore pulling the sliding block to the left. The center gravity of the glider moves to the left of the center line. The glider’s attitude moves to the left. When the glider gets to the lowest point, the system automatically cut off the current of the left wire to cool it and meanwhile the right wire is heated by electrifying. At the same time, the left SMA wire phase converts from austenite to martensite and the right SMA wire phase converts from martensite to austenite, which makes the left sliding block move back to the original position and the right sliding block move to right. Then, the center gravity of the glider moves to the right of the center line and the glider’s attitude moves to the right. Depending on the pulse current, the glider can achieve attitude regulating in the water. 4. System Modeling The system is completely symmetry from the center line. The scheme of the system is shown in Fig. 3. There is a mass block connected to a SMA wire. The SMA wire has an initial length L with an extending L . The purpose of current investigation is to show the response effect of the attitude regulating system. Fig. 3. Simplified physical model of the attitude regulating system. Here for the mass block, the model can be easily formulated using the momentum conservation law as: M M MMx x kx f    , (1) where Mx is the position of the block;  is the viscosity coefficient; k is the spring stiffness; M is the mass of the block, and f is the force on the mass block which is caused by the SMA wire discussed below. It can infer from the Fig. 3 that Mx L . Sensors & Transducers Journal, Vol. 16, Special Issue, November 2012, pp. 1-10 4 According to the thermodynamic principle and electrical resistance principle, we can get:     2 0 dT mc Ri t hS T t T dt    , (2) where m is the mass of the SMA wire; c is the specific heat of wire; R is the electrical resistance of the SMA wire; 2 = 4 DS  is the cross-sectional area of the SMA wire; h is the heat convection coefficient. Brison’s model is now the most widely used SMA model .It is based on the Tanaka’s model and Liang-Rogers’s model. Brison’s model phase transformation equation: Austeniteto stress induced martensite: If ST M and    cr cr S M S F M SC T M C T M                0 01 1 cos 2 2 crS S S F M Scr cr S F C T M                 (3)  0 0 0 01 T T T S S S          , (4) where cr S and cr F are the critical stresses at the start and finish of the conversion of the martensitic variants; MC is the material properties that describe the relationship of temperature; SM and FM are the martensite phase start and final temperature respectively. Compound twin martensite to stress induced martensite, ST M and cr cr S F    :  0 01 1 cos 2 2 crS S S Fcr cr S F               (5)  0 0 0 01 T T T S S S T            (6) where   01 cos 1 2 T M FT a T M        at 0&F SM T M T T   . When Compound twin martensite or stress induced martensite transform into austenite, its volume fraction changes as: If ST A and    A F A SC T A C T A    : Sensors & Transducers Journal, Vol. 16, Special Issue, November 2012, pp. 1-10 5 0 cos 1 2 A S A a T A C               (7)  0 0 0 0 S S S         (8)  0 0 0 0 T T T         (9) whereT is the SMA wire temperature; SA and FA are the austenite phase start and final temperatures;  A F Sa A A  and AC are curve fitting parameters. By substituting S T    into Liang’s one dimension model, the equation can be obtained as follows: S T S T d d d d dT T                       (10) It can also be described as: S S T Td Ed d d dT       (11) Set material parameters , , ,S TE    as constant and the initial condition as 0 0 0 0 0, , , ,S T T    . Following equation can be obtained by integrating Eq. (11):        0 0 0 0 0S S S T T TE T T                , (12) where E is the Young modulus;  is the phase transformation contribution factor;  is the thermal expansion factor; T is the fraction of the material that is purely temperature-induced martensite with multiple variants. At the beginning of the phase transformation, the martensite volume fraction is 100 %, so 0 0S  and 0 0T  .Set 0 0 0   , 0  , L  , 1S  , 0T  and  0 S ST T M T A   , the following relation can be obtained: S LE   , (13) where L is the largest restoring strain. Then Eq. (12) can be simplified as:      0 0 0 0L S SE E T T             (14) 5. Experiment and Model Simulation An experiment has been carried out to demonstrate the high response of the new attitude regulating system of the underwater glider. The effective length of SMA wire in the new attitude regulating Sensors & Transducers Journal, Vol. 16, Special Issue, November 2012, pp. 1-10 6 system is only 40 mm. The experiment which is reported in this section has been performed on a TiNi SMA wire with length of 350 mm for convenience. At first we fixed the SMA wire on the device like Fig. 4. Then we extend the wire to another 7 mm (2 % of the original length) and set the objective force 100 N, which means that the wire is always suffered from a 100 extruding force in the process of contracting. The current from 1.0 A to 2.5 A is given to the wire through PS 8000 2U power supply. After the wire have completely contracted, the current is cut off in order to make the wire cool off in the air and extend to original displace under the 100N objective force. The experiment date can be obtained by NI M-series DAQ. (a) (b) (c) Fig. 4. The experiment equipment including: PS 8000 2U power supply (a); SMA fixing device (b), and NI M-series DAQ (c). The experiment results are shown in Table 1 and the relationship between time and displacement is present in the Fig. 5. Table 1. Part of the experiment data under different current. 1 A 1.5 A 2 A 2.5 A Time, （s） Displacement, （mm） Time, （s） Displacement, （mm） Time, （s） Displacement, （mm） Time, （s） Displacement, （mm） 20 0.4665 20 0.5309 40 0.5063 20 0.5468 26 0.9032 23 0.8653 42 0.8875 21 0.6412 32 1.4399 26 1.3712 44 1.8133 22 0.9244 38 2.0044 29 2.1059 46 3.0132 23 1.2522 44 2.5189 32 2.9486 48 4.6895 24 1.7817 50 3.1581 35 3.7847 50 4.9096 25 2.4365 56 3.5852 38 4.4976 52 5.4391 26 3.2594 62 4.0482 41 5.0078 54 5.7671 27 4.0517 68 4.4781 44 5.4314 56 5.9279 28 4.8589 74 4.8935 47 5.7992 58 6.0787 29 5.2117 80 5.2357 50 6.0877 60 6.1537 30 5.4893 86 5.5429 53 6.2616 62 6.2679 31 5.6703 92 5.7911 56 6.4418 64 6.3437 32 5.8144 98 6.0466 59 6.5142 66 6.3792 33 5.9468 104 6.1807 62 6.5352 68 6.4177 34 6.0141 110 6.2683 65 6.5354 70 6.4368 35 6.0421 116 6.3317 68 6.5355 72 7.44448 36 6.1170 Sensors & Transducers Journal, Vol. 16, Special Issue, November 2012, pp. 1-10 7 The data in Table 1 are taken into Matlab and the following curve Fig. 5 can be obtained. (a) I=1 A (b) I=1.5 A (c) I=2 A (d) I=2.5 A Fig. 5. Time-displacement curve under different current. We can get from the Fig. 5 that the SMA wire contracts very quick under the effect of different current and restores to its original shape completely. With the increasing of the current, the SMA wire shows high contract speed and short response time with stronger response stress. However, the current shouldn’t be too heavy, which will make the wire so hot that decrease the steady of response stress. In addition, it can be inferred from Fig. 5 that the experimental data shows an exponential distribution that is there is an exponential relationship between phase transformation and heating current. We use the same load, heating current and SMA wire parameter during simulation and experiment, In order to have a comparability of quantification with feasibility of experiment between simulation of SMA wire driving and experiment result. All simulations reported here have been carried out for NiTi SMA wire with a length of 354 mm. its physical parameters are available as follows: o o o o o2 6 C , 4 0 C , 6 5 C , 8 0 C ,C 2 8 / Cf s s f MM M A A M P a     o o 7C 40 / C, 1.5 / C, 8.0 10A MPa MPa resistivity      Sensors & Transducers Journal, Vol. 16, Special Issue, November 2012, pp. 1-10 8  3 3 2 26.5 10 , 460 / , 70 / ( )density kg m c J m C h W m C         0.3048 , 35917 , 16800A MD mm E MPa E MPa   Computer simulations of the SMA model were performed in MATLAB/Simulink by using Eq. (2) and Eq. (12) and then Fig. 6 can be obtained. It shows that the result of experiment and simulation are almost the same but only have little different at the beginning. Fig. 6. Simulation and experiment comparison curve. The experiment result of displacement-time curve shows that at the beginning of electrifying, SMA wire stays in martensite phase. The SMA wire has a prestretch suffering from the force sensor, so the Fig. 6 has a downturn process. With the increasing of the SMA wire temperature, it will change from martensite to austenite and the wire start to contract, so the curve shows the ascending process. The phase transformation is very fast which only needs little time. When SMA wire turns into austenite completely, it will stop contracting and the curve presents horizontal extending. According to the Fig. 6, there is little difference between simulation and experiment result, but it is basically the same in the process of deformation restoring. Therefore, SMA is the best material used to driving attitude regulating system which will make the underwater glider response fast with high efficient. In the end, the current is given to TiNi SMA wire of the new attitude regulating system to test whether it can regulate attitude or not. First time when the current is given to the wire, the wire contracts to the original shape and pull the adaptive block to the one side, which, on the other hand, change the gravity center of system to one side. So the whole attitude regulating system tilts to one side, realizing the goal of regulating attitude of system. The attitude of the new system before and after electrifying can be seen in Fig. 7 (a) and Fig. 7 (b). It can be inferred from Fig. 7 that the new designed attitude regulating system satisfies our requirements of regulating attitude quickly and precisely and the TiNi SMA wire is the most suitable material to driving the system. Sensors & Transducers Journal, Vol. 16, Special Issue, November 2012, pp. 1-10 9 Fig. 7 (a). Before electrifying. Fig. 7 (b). After electrifying. 6. Conclusion A novel design of shape memory alloy based attitude regulating system is developed in this paper. The dynamic model of attitude regulation system is established based on the SMA constitutive model. An experiment is finished, which shows the new designed attitude regulation system satisfy the requirement of the underwater glider. In addition, SMA wire is the most suitable material to drive the attitude regulating system. Acknowledgement The authors acknowledge the support by the Key Science and Technology Innovation Team Project Grants of Zhejiang Province (Grant No. 2010R50003). Reference [1]. T. Curtin, J. G. Bellingham, J. Catipovic, and D. Webb, Autonomous Ocean sampling networks. Oceanography, 1993, 6(3), pp.86-94. [2]. C. C. Eriksen, T. J. Osse, R. D. Light, T. Wen, T. W. Lehman, P. L. Sabin, J. W. Ballard, and A. M. Chiodi, Seaglider: A longe-range autonomous underwater vehicle for oceanographic research, IEEE Journal of Oceanic Engineering, 2001, October , Vol. 26, pp. 424-436. [3]. Huber, J. E., Fleck, N. A. Landis, C. M. McMeeking, R. M., A constitutive model for ferroelectric polycrystals, Journal of the Mechanics and Physics of Solids, 1999, 47, pp.1663-1697. [4]. Kamlah M., Ferroelectrin and ferroelastic piezoceramics-modeling and electromechanical hysteresis phenomena, Continuum Mechanics and Thermodynamics, 2011, 3, pp. 219-268. [5]. Landis C. M., Fully Coupled, Multi-Axial, Symmetric Constitutive Laws for Polycrystalline Ferroelectric Ceramics, Journal of the Mechanics and Physics of Solids, 2002, 49, pp.785-811. [6]. Landis C. M, and McMeeking R. M, A Phenomenological Constitutive Law for Ferroelastic Switching and a Resulting Asymptotic Crack Tip Solution, Journal of Intelligent Material Systems and Structures, 1999, 10, pp.155-163. [7]. R. Abeyaratne, S. J. Kim, and J. K. Knowles, A one-dimensional continuum model for shape-memory Sensors & Transducers Journal, Vol. 16, Special Issue, November 2012, pp. 1-10 10 alloys, International Journal of Solids and Structures, 1994, 31 (16), pp.2229-2249. [8]. Guo Shuxiang, Liwei Shi, and Lingfei Li, A New Type of Jellyfish-Like Microrobot, in Proceedings of the International Conference on Mechatronics and Automation (ICMA' 2007), August 2007, pp. 509-514. [9]. Villanueva, A. A., K. B. Joshi, J. B. Blottman, and Priya S., A bio-inspired shape memory alloy composite (BISMAC) actuator, Smart Materials and Structures, 2010, 17. [10]. Yeom, Sung-Weon, and Il-Kwon Oh., A biomimetic jellyfish robot based on ionic polymer metal composite actuators, Smart Materials and Structures, 2009, 15. ___________________ 2012 Copyright ©, International Frequency Sensor Association (IFSA). All rights reserved. (http://www.sensorsportal.com) http://www.sensorsportal.com/HTML/Sensor.htm http://www.sensorsportal.com/HTML/E-SHOP/PRODUCTS_4/UFDC_1.htm Sensors & Transducers Journal, Vol. 16, Special Issue, November 2012, pp. 11-17 11 SSSeeennnsssooorrrsss &&& TTTrrraaannnsssddduuuccceeerrrsss ISSN 1726-5479 © 2012 by IFSA http://www.sensorsportal.com Study on the Vibration of the 1700 Four Rollers Rolling Mill 1 Zehao Wu, 1 Gongfa Li, 1 Po Gao, 1 Yuan He, 1 Dawei Tan 1 College of Machinery and Automation, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China Tel.: 086-02768862283 E-mail: zehaowu@163.com Received: 11 September 2012 /Accepted: 11 October 2012 /Published: 20 November 2012 Abstract: Aimed at the vibration problem of the rolling mill, by using the large FEM software ANSYS, modal analysis of the rolling mill is did. The natural frequency and the vibration models of rolling mill model are obtained. Analysis the simulation results, the causation of the vibration is found, and the solutions are proposed, which provides references for the designing of mill and restraining mill vibration. Copyright © 2012 IFSA. Keywords: Rolling mill, Vibration, Modal analysis, ANSYS. 1. Introduction The vibration of rolling mill is a long plagued problem in mill industry. The losses are very serious cased by the vibration of rolling mill. Such as rolled strips appear chatter marks, and the quality cannot meet the production standards. Others components of rolling mill are damaged. Not only the efficiency of production is reduced, but also the safety of staffs is threatened. As there are so many parts on mill and the assembly of those parts is complex, so there are much vibration signals when the mill is working. The transitional vibration and noise is eliminated after the researching of vibration, and then the frequency of excitation is minimized. The signal measurements are simplified, and the analysis of problem becomes efficient, by using the finite element analysis software of ANSYS. The dynamic vibration modals of rolling mill are achieved on the software platform. The performance of every parts of rolling mill is descried by the clear dynamics cloud when rolling mill is motivated [1]. The location and type of vibration source is determined by the result of modal analysis, which provides a powerful theoretical foundation for the inhibitor and avoiding of mill vibration [2]. http://www.sensorsportal.com Sensors & Transducers Journal, Vol. 16, Special Issue, November 2012, pp. 11-17 12 2. The Characteristics of the Chatter Marks on Backup Rolls Surface This is a 1700 four rollers rolling mill, its barrel length is 1700 mm, and started from 1978. The main components of rolling mill: the aches, the pressure device, the backup rolls, the balance tanks, the working rolls, the replacing roll device, the locking device and the wedge. When it is working at the stable speed of 1000 m /min, the vibration of rolling mill is significant. Chatter marks appear on backup roll surface, the chatter marks shown as Fig. 1. Fig. 1. The shape of chatter marks on backup rolls surface. The Chatter marks parallel to the axis of roll. The lights and darks are alternated. The distance between light and dark is uniformity, and approximately is 138.8 mm. The intensity of chatter marks is different generally, no obvious law. The brightness of stripes can be transformed each other with the changement of the observation and illumination angle. The light stripes are changed into the dark stripes, while the dark stripes are changed into the light stripes. The locations of chatter marks on backup roll surface are not fixed. And chatter marks are shifted on the circumuferential direction of roll surface. The roughness of the light filed or the dark filed is the same. There is a close correspondence between the distance of chatter marks, the rolling speed and the vibration frequency [3]. The stable rolling speed is 1000 m /min, while the distance of chatter marks is 138.8 mm. So the contact state between the working rolls and the backup rolls presents a certain periodicity. And the vibration frequency of backup rolls is ： 120f Hz     , (1) where v is the stable rolling speed;  is the distance of chatter marks; f is the vibration frequency. With the mill is working, the surface of rolled strips also appear irregular chatter marks. The quality of rolled strips is reduced, and the backup rolls are damaged. There is a close association between the vibration and chatter marks. To reduce the losses cased by chatter marks, it is need to determine the location and type of vibration. To find the reason why the vibration frequency 120 Hz is caused, modal analysis of rolling mill. Sensors & Transducers Journal, Vol. 16, Special Issue, November 2012, pp. 11-17 13 3. The Theory of Modal Analysis The classic definition of modal analysis: in order to find the modal parameters of the system, the physical coordinates in the differential equations of vibration in a linear constant system is transformed into the modal coordinates, which decouples the equations. It is an independent equation described by the modal coordinates and the modal parameters [4]. The coordinate transformation matrix is called the modal matrix that each column is the mode shape. [ ]{ } [ ]{ } [ ]{ } [ ]M x C x K x Q    , (2) where [ ]M is the mass matrix of system; [ ]K is the stiffness matrix of system; [ ]C is the damping matrix of system;  x is the acceleration column vector of system; { }x is the velocity column vector of system; { }x is the displacement column vector of system; [ ]Q is the loading matrix. The modal parameters of the system are identified, which is the ultimate goal of modal analysis. The basis for the vibration analysis of structural systems, the diagnosis and prediction of vibration fault and the properties optimization of structural dynamic are provided by modal analysis. So in order to understand and analyze the mechanism of the natural frequency characteristics, and simulate their dynamic behavior, modal analysis of rolling mill is needed [5]. 4. The Modal Analysis of Rolling Mill 4.1. Establish The Model of Rolling Mill The location of vibration source can not be got clearly, so the whole rolling mill model established. With the sheets of this mill, the components of rolling mill are drawn by the three-dimensional mapping software of PROE. And the three-dimensional solid model of rolling mill is shown as Fig. 2. Fig. 2. The model of rolling mill in PROE. Because the size of rolling mill is large, the structure and the assembly is complex, so it is difficult to establish the model in the pre-processor of ANSYS, otherwise the expression of the structure is Sensors & Transducers Journal, Vol. 16, Special Issue, November 2012, pp. 11-17 14 incomplete [6]. Before doing the modal analysis, the model of rolling mill can be simplified base on the three-dimensional solid model of rolling mill. Then change into the IGES format (the compatible file format of ANSYS). The main predigested parts are the pressure system of rolling mill, the oblique lease system of rolling mill, the roller device of the under roll. Expert these, the two pieces arches, the beam, the roll systems of rolling mill and the balance cylinder are remains the same with sheets of rolling mill. And the finite element model of rolling mill is shown as Fig. 3. Fig. 3. The model of rolling mill in ANSYS. 4.2. Modal Analysis in ANSYS Importing the finite element model into ANSYS, and began doing the model analysis of rolling mill. The properties of material should be defined at first. The elasticity coefficient is 2.1E5 MPa, the Poisson’s ratio is 0.3, and the density is 7.8E-9 t/m3. As the model is solid and complex, so the entire structure meshed by 8-node hexahedral element Brick 8 node 45, which is SOLID 45 element. The meshed map of rolling mill is shown as Fig. 4. Fig. 4. The meshed map of rolling mill. Sensors & Transducers Journal, Vol. 16, Special Issue, November 2012, pp. 11-17 15 The loading of constraints are determined by the installation of rolling mill in the actual and the force in the working process. There are eight bolts used to fix rolling mill, so in this connection at the surface of the eight bolts’ hole plus all constraints, the displacement of rolling mill in X, Y, Z directions is 0. Subspace is suitable to get less vibration mode (< 40) of the models that is medium or not large. The needed memory is relatively small, if this method is used. The solid elements and the shell elements of the unit should have a better shape in Subspace. And any warning message about the shape of the unit should be attention. As a rigid body vibration mode convergence problems may occur, so it is used in the situation that there is no constraint equations [7]. Modal analysis of ANSYS is a linear analysis, any non-linear characteristics like the elastic, the plastic and the element of contact gap will be ignored, even if these are taken into the analysis. The non-linear characteristics are directly converted into linear terms, so the non-linear characteristics are not considered in the post-processing of modal analysis [8]. So the exposure in modal analysis is GLU. The 1st to 10th vibration modes are extracted in modal analysis of rolling mill, and the results are shown as Table 1. Table 1. The modal results of the top 10 steps. SET 1st 2nd 3rd 4th 5th FREQ (HZ) 21.9 36.3 46.7 78.7 89.6 SET 6th 7th 8th 9th 10th FREQ (HZ) 116.3 142 176.2 240.5 469.5 The vibration patterns are be shown by the cloud model, and the DMX also can be get from the cloud model. All of these can be used to judge whether rolling mill is working properly or not. The vibration diagrams of 1st to 6th are shown as Fig. 5. 4.3. Analysis of Simulation Results Known by the calculation results, the vibration frequency of backup rolls 120 Hz is very close to the natural frequency of 6th 116.3 Hz. And shown by the 6th vibration mode, the two working rolls of rolling mill are moved in the opposite direction. On the same side, the working roll and the backup roll are moved in the opposite direction. All of those consistent with the actual working conditions of the rolling mill. So it is considered that the natural frequency of 6th is caused by the vibration frequency of backup rolls. When the vibration frequency of backup rolls reaches 120 Hz, approaching the natural frequency of 6th, the rolling mill is occurred resonance [9]. And the surface of backup rolls appears irregular chatter marks. Then the vibration of working rolls is incentived by the chatter marks on backup rolls surface, which leads to forced vibration of working rolls. And the chatter marks become more obvious. Based on the above analysis, the restraining measures of chatter marks are obtained. The countermeasures are derived as follows: adding roll to control vibration in the inlet side or changing the speed of stable rolling. The relatively movement between the rolls system is caused by the fluctuation of entry tension, which also is the reason why chatter marks appear on the surface of rolls surface [10]. So adding roll to control vibration in the inlet side that the resonance is suppressed and eliminated, and the chatter marks are Sensors & Transducers Journal, Vol. 16, Special Issue, November 2012, pp. 11-17 16 eliminated. because the chatter marks are caused in the speed of stable rolling, so changing the speed, the vibration frequency of backup rolls should not approached the natural frequency of 6th, the rolling mill is not occurred resonance. Last, the chatter marks are eliminated. (a) The 1st vibration mode. (b) The 2nd vibration mode. (c) The 3rd vibration mode. (d) The 4th vibration mode. (e) The 5th vibration mode. (f) The 6th vibration mode. Fig. 5. The vibration cloud modes of 1st to 6th. Sensors & Transducers Journal, Vol. 16, Special Issue, November 2012, pp. 11-17 17 5. Conclusions In this paper, the three-dimensional solid model of a factory’s four-roll rolling mill is established, which clearly expresses the structure of rolling mill. The late, the simulations of roll motion, pressure action and rolling are achieved based on this model. The natural frequencies and the mode shapes of rolling mill are got after modal analysis, which is the foundation of the dynamic analysis of rolling mill. The results can be used to judge the capper marks are caused by the vibration of backup rolls. The causation of the vibration is found, and the solutions are proposed, which provides references for the designing of mill and restraining mill vibration References [1]. Guo R M, Stress analysis and life expectancy of rolling mill housings, Iron and Steel Engineer, Vol. 69, Issue 8, 2008, pp. 45-53. [2]. Lifang Cai, Jie Zhang, Jianguo Cao, Study on the wear of the working roll in temper rolling mill for hot strip, Metallurgical Equipment, Vol. 4, Issue 2, 2007, pp. 38-41. [3]. Fuxiang Hou, Jie Zhang, Xiaolu Shi, Measurement of chatter marks on the backup roll of a cold temper mill, Journal of University of Science and Technology Beijing, Vol. 29, Issue 6, 2007, pp. 613-616. [4]. Huaimin Wang, Zhengshi Liu, Analysis of dynamic characteristics of the four-roller hot strip mill frame, Journal of Hefei University of Technology, Vol. 32, Issue 3, 2009, pp. 351-354. [5]. Baoqiang Zhou, Yingwu Lai, Dechen Zhang, Research on calculation of the frame natural frequency of 1780 mm rolling mill, Heavy Machinery, Vol. 1, Issue 3, 2010, pp. 24-27. [6]. Dianping Yin, Zhihui Sun. Roller system vibration research of two-stand cold continuous rolling mill, Metallurgical Equipment, Vol. 2, Issue 1, 2010, pp. 21-24. [7]. Houzhuang Ai, Dechen Zhang, Han Tang, Investigation of dynamic characteristics for 1700 four rollers strip mill, Journal of Anshan University of Science and Technology, Vol. 29, Issue 5, 2006, pp. 459-462. [8]. Deqiang Ju, Wei Zhao, Furao Jiang, Finite element analysis on temper mill housing, Heavy Machinery, Vol. 1, Issue 2, 2011, pp. 52-55. [9]. Xuetong Li, Zhihe Wu, Fengshan Du, Jingna Sun, FEA on rolls’ deformation in rolling process of 4-roll skin miller, Journal of Plasticity Engineering, Vol. 15, Issue 2, 2008, pp. 126-130. [10]. Peilin Chen, Zeji Wang, Cause and control of chatter marks on steel strip surface, Iron and Steel, Vol. 41, Issue 5, 2006, pp. 49-52. ___________________ 2012 Copyright ©, International Frequency Sensor Association (IFSA). All rights reserved. (http://www.sensorsportal.com) Sensors & Transducers Journal, Vol. 16, Special Issue, November 2012, pp. 18-26 18 SSSeeennnsssooorrrsss &&& TTTrrraaannnsssddduuuccceeerrrsss ISSN 1726-5479 © 2012 by IFSA http://www.sensorsportal.com The Design of Wireless Sensor Application System Based on Internet of Things 1 Zhang Ye, 1, 2 Zhang Feng, 1 You Fei 1 School of Information Engineering, Yulin University, 719000, Yulin, China Tel.: +8613649220169 2 School of automation, Northwestern Polytechnical University, 710072, Xi’an, China Tel.: +8613891915616 E-mail: 876371599@qq.com, tfnew21@sina.com Received: 11 September 2012 /Accepted: 11 October 2012 /Published: 20 November 2012 Abstract: In order to improve agricultural irrigation water use efficiency, reduce cost of agricultural irrigation water, this paper discussed the design of wireless sensor network and Internet technology of farmland automatic irrigation control method. The system hardware, software designed, middleware, and applications such as mobile phone or wireless PDA of Internet of Things of the routing protocol of sensor network nodes were analyzed, and constitute a variety of sensors intelligent network, thus the overall automation system and monitoring levels were enhanced. Final the network in the Internet based on the agricultural plants of farmland water-saving irrigation system integrated approach were analyzed. User use mobile phones or wireless PDA can easily soil moisture content of online monitoring and control to realize the irrigation automation. Application results show that system through the embedded control technology complete intelligent irrigation, the agricultural irrigation water use efficiency was improved, irrigation system automatization is generally low status, can well realize water saving. Copyright © 2012 IFSA. Keywords: Wireless sensor, Internet of Things, Routing protocol, Localization of nodes, RFID. 1. Introduction As new Internet of Things information network, for most types of agricultural materials, agricultural products through the Internet of Things will be fresh growth state, response to environmental changes, storage preservation, distribution and quality and safety of equipment, machinery, people close integration of active behavior, will have an important impact on the agricultural economy [1]. http://www.sensorsportal.com Sensors & Transducers Journal, Vol. 16, Special Issue, November 2012, pp. 18-26 19 With the development of Internet of Things, its technology has been widely applied to all aspects of agricultural production, water-saving irrigation involves engineering, agriculture, biology, automation, communications, and many other technologies. water-saving irrigation automatic control system based on wireless sensor using the sensor and set the conditions and the receiver communication, control irrigation systems, valves open, close, so as to achieve the purpose of automatic water-saving irrigation. Because of sensor networks with multiple hops routing, information exchange recursion, self-organizing networks and network communication time synchronization characteristics and irrigation area, the number of node cannot restricted, and thus can be flexible increase or decrease round irrigation group. And nodes with soil, plants, weather measurement acquisition device that uses communications gateway Internet functions and GPS techniques combined to form the dynamic management information collection and analysis irrigation technologies, along with crop water information collection and semi-precise control irrigation technique, expert system technology, etc., can construct high efficiency, low power consumption, low investment, multi-function agricultural water-saving irrigation platform [2]. Users can also in a greenhouse, courtyard garden green, the central isolation belt highway, agricultural wells and other areas with irrigated areas, agricultural and ecological water-saving technology, quantification, standardization, modeling, integration, and promote the rapid and water-saving agriculture healthy development. In the Internet of Things related techniques, domestic currently in wireless sensor network software made corresponding breakthrough on the operating system based on foreign to develop their own middleware software. Such as the Nanjing University of Posts and Telecommunications research center for development of wireless sensor networks based on mobile agent middleware platform for wireless sensor networks, shenglian technology development of wireless sensor network development kits. Domestic research institutions in the theoretical research, such as network protocols for wireless sensor networks, algorithms, architecture, etc., put forward a number of innovative ideas and theories. In this field, Nanjing University of posts and telecommunications, Tsinghua University, Beijing University of Posts and so made some relevant theoretical research results [3]. In other countries, many American universities in the wireless sensor network have carried out a lot of work. Such as the University of California, Los Angeles, CENS (Center for Embedded Networked Sensing) Laboratory, WINS (Wireless Integrated Network Sensors) Laboratory and IRL (Internet Research Lab) and so on [4]. This paper reference some enterprise's actual production processes, from home and abroad to the traceability system related research, through research based on internet of things water-saving irrigation system scheme, the original traceability system based on Web water-saving irrigation processing scheme must be improved and in practical projects application and achieved good effect. Automatic irrigation control system has the following advantages: 1) Will give full play to the role of the existing water-saving devices, optimal operation and improve efficiency; 2) Through the application of automatic control technology, more water and energy conservation, reduce irrigation costs and improve the quality of irrigation; 3) Will irrigate more scientific, to facilitate and improve the management level. Control the development and promotion of water-saving irrigation technology is the need for agricultural modernization. Sensors & Transducers Journal, Vol. 16, Special Issue, November 2012, pp. 18-26 20 2. Design of Water-saving Irrigation Automatic Control System 2.1. Wireless Sensor Networks Wireless sensor network technology applied to the water system, its core technology is the application of ZigBee networking technology since. ZigBee is a low heterogeneous, low power, low data rate, low capital, high-solid reliability, a large network capacity two-way wireless communications technology [5]. By using the layer, network layer, medium access control layer and physical layer. Based on ZigBee wireless sensor networks system can solve the cable transmission bring cost is exorbitant, cabling complex, maintenance trouble, flexibility and expansibility such a series of problems, which saves the human resources, went to the lavatory again information management, has been used steel temperature monitoring, vegetable shed steelmaking temperature, humidity and soil ph monitoring, gas meter and other fields, it is the realization of the system provides a better solution. 2.2. System Architecture Design The system uses single chip control center, by wireless sensor node (RFD), wireless router nodes (FFD), wireless gateway (FFD), the monitoring center four local compositions, accepted the ZigBee since network, the monitoring center, wireless gateway between resolution GPRS hold moisture content and control information transfer. Each sensor node resolved temperature and humidity sensors, soil moisture information automatically collected and combined upper and lower limits of humidity at the default analysis, determine what can and cannot have irrigation hour suspension. Each node with a solar battery, the battery voltage is monitored at any time, once the voltage is too low, the node will issue a low voltage alarm signal sent successfully, the node into the sleep pattern until it is fully charged. Which wireless gateway cohesion ZigBee wireless network and GPRS networks, is based on wireless sensor networks of water-saving irrigation control system, as the center of local wireless sensor node maintenance. Autonomous sensor nodes and routing nodes form a multi-hop network. Monitoring of temperature and humidity sensors distributed in the region, the collected data sent to the nearest wireless routing node, the routing node routing algorithm based on selecting the best route, and establish the appropriate routing list, which list contains the information itself, and the neighbor gateway information. Then data has been sent to remote monitoring center by protocol gateway, convenient for the user to remote monitoring maintenance. The water-saving irrigation automatic control system based on the Internet of Things has been shown in Fig. 1. 2.3. System Hardware Design Main achievement of the system sensor nodes monitoring soil water content, EC (conductivity) value and the PH value of the monitoring, monitoring of the state of the solenoid valve, solenoid valve to control the state, various monitoring and control of transmission of communication signals and low voltage alarm. Each control unit controls the 1-6 way solenoid valves. Through the sensors to collect to of multi-channel data, after A/D conversion, signal processing, in microprocessor, according to different vegetation needs, identifying water amount, then control signal output, combining central management of A computer instruction, control the solenoid switch, namely can achieve automatic irrigation. Soil moisture sensor used to measure the soil moisture, in order to understand the real irrigation, soil basis, determined irrigation or not and duration, equipped with EC value and PH sensors, may to the inlet and outlet water for EC value and pH value detection, in order to control automatically nutrient rationing. The main block diagram has been shown in Fig. 2. Sensors & Transducers Journal, Vol. 16, Special Issue, November 2012, pp. 18-26 21 Fig. 1. Wireless sensor system structure. Fig. 2. Sensor nodes hardware control structure. 2.4. System Software Design The system uses Internet environment, the use of B/S model for development. System server operation system chooses Linux, the main technology used for the Java EE and Java programming language, database system used Oracle11g. Real-time data processing system database is the core of the whole. Real-time database as an intermediary link, the data situation in order to realize the scene is reflected in the form of animation on the screen, making the operator's instructions before the computer can quickly reach the scene. The software structure has been shown in Fig. 3. Sensors & Transducers Journal, Vol. 16, Special Issue, November 2012, pp. 18-26 22 Fig. 3. Structure of software. 3. Implementation of Water-saving Irrigation Automatic Control System 3.1. Design of Sensor Data Processing Middleware Data acquisition and control function, can pass hardware driver and equipment communication, complete data acquisition and control tasks, sensor middleware function modules should contain the following several function module: Reader interface module, the logical drive mapping module, sensors data filtering module, the business rules, filter modules, equipment management and configuration module, the upper service interface module [6-7]. And, Reader interface for middleware and sensors reader data communications, mainly to have access to sensors data and device management module to issue commands reader. Equipment management configuration module is used to adjust the working status of sensors read-write device, configure the appropriate interface parameters such as Reader, logical reader will be more physical mapping module reader for the reader or multiple antennas into a logical map reader. Control procedures and parameter Settings by sensor data processing middleware provided in the programming language scripts in writing. Run-time system control program flow chart has been shown in Fig. 4. 3.2. Sensor Network Routing Algorithm Routing protocol to solve the data transmission problem, which is the core technology of wireless sensor networks and the key technologies to ensure network performance. Since wireless sensor network node capacity constraints, the traditional IP network routing protocols are generally not suitable for wireless sensor networks [8-9]. Wireless communication network of the traditional focus on wireless communications service quality (Qos) on the [10-11], while the WSN node energy as the limited bandwidth resources are limited, the routing protocol research focus on how to improve energy efficiency and maintenance of load balancing on. In order to meet the unique needs of wireless sensor networks, MIT's Heinzelman, who developed a low-energy adaptive. Clustering routing protocol should be LEACH, LEACH is the first cluster based on multi-cluster structure of the routing protocol, compared with the traditional protocol, which can better save energy. LEACH's cluster approach through its many levels, after the proposed routing protocol, then a lot of clustering routing protocols (such as TEEN, PEGASIS) are developed on its basis, its results also apply to many other routing protocol. So, the main research and discussion on the LEACH protocol [12]. Sensors & Transducers Journal, Vol. 16, Special Issue, November 2012, pp. 18-26 23 Fig. 4. Control procedure flow chart. Due to the middle of wireless sensor network by the design of first consideration is energy problem, it is necessary to study channel energy loss model. Different communication characteristics, it will have different energy consumption model. Channel energy model with free-space propagation model and multiple attenuation model two. If the receiving node and the distance between the sending node is less than some critical value, free space model is used; otherwise, the use of multiple attenuation model. Critical value is defined as follows: 0 4 r tah h d    , (1) where α is the path loss exponent; hr is the receiver antenna height; ht is the height of transmitting antenna; λ is the wavelength of the signal. When the sending node k bit data to the associated transmission distance d of the receiving node, by the following formula to calculate energy consumption of transmitting nodes:      , ,Tx Tx elec Tx ampE k d E k E k d   2, 0 4, 0 E k k d d d elec fs E k k d d d elec mp                , (2) where, ETx-elec(k) is the emission k bit data transmitter circuit energy consumption; ETx-amp(k,d) is the emission k bit data transmission distance d and the energy consumption when the power amplifier. Eelec bit of data for each circuit in the transmitting or receiving the energy consumed by the circuit. Constants fs, and mp, and the transmission channel model used are related to a free space transmission of the fs, multi-path fading transmission of the mp. Loss of signal strength and transmission are distances related. When the transmission distance is relatively close, the use of free-space propagation model, path loss exponent of 2, when the transmission distance is relatively far, the use of multiple attenuation model, path loss exponent is 4 [13]. Sensors & Transducers Journal, Vol. 16, Special Issue, November 2012, pp. 18-26 24 All nodes LEACH self-organization cluster in clusters, by a node as cluster head, all cluster member node sent to cluster head, cluster head of data fusion processing backwardness to base station. Cluster head than cluster nodes consume more energy members. By used a random rotation of cluster head node mechanism to avoid depletion of energy in the LEACH routing protocol. 3.2.1 Proportion of Stages Cluster Head The clusters were established at the time in the first network, to elect the current round of the cluster head node. Specifically, each node in the 0 to 1 to select a random number, if the random number than the predetermined threshold T (n) is small, then the node for the cluster head in the current round; otherwise, in the current round of the cluster members. T (n) is set to    11 . mod( ) , 0 p p r n Gp T n         (3) where, p is the desired percentage of cluster head nodes, usually taking the total number p of nodes around 4 % -5 %; r is the current number of rounds; G is in recent not become before 1mod( )r p wheel cluster head node collection. 3.2.2. The Organizational Phase of Clustering This stage, each node must determine if they should act as the first round of the cluster nodes. Determine the cluster head immediately after the broadcast packet to the network; the information package contains the node's own ID. Each node according to the received signal strength, choose the source node sending the strongest signal as their cluster head node. Once the decision as to which cluster node after the node through the carrier sense multiple access (CSMA, Carrier-Sense Multiple Access) MAC protocol that information to the cluster head, cluster head node notify its members that he would become one of information includes its own ID and the cluster head node ID. 3.2.3. The Organizational Stage Cluster Creation Timetable Cluster head according to oneself the number of cluster nodes in creating a time table, and through broadcast announcement each cluster nodes in the data transfer time. Using time buttress table can make cluster member node data transmission won't collisions and conflicts, and in the data transmission period, node can shut down their wireless device, will it be more energy saving. So far, the stage of establishing a cluster finish began to stabilize the data transmission phase. 3.2.4. Stability Phase In the stable data transmission phase, cluster member in the time of their own to cluster hair send sensor data. Cluster head nodes have been let oneself of the receiver is open, for which can receive from different cluster member of data. When a frame of data has been transfer after the cluster head node of data fusion and compression, the compression process after the signal transmission give base station. Sensors & Transducers Journal, Vol. 16, Special Issue, November 2012, pp. 18-26 25 The processes described above are all cluster communication within the cluster to neighboring clusters will inevitably affect the work. In order to reduce interference between clusters, different cluster communication can be used Code Division Multiple Access (CDMA) system. Cluster head node from a group of spreading codes selected code as an extension of the cluster ID, and notify all members of the cluster nodes. So, when the cluster communications, according to the different ID, the other clusters will be filtered out of signal. 4. Performance Tests In order to verify the improve performance of routing protocols, made by computer simulation of the algorithm. Simulation network model used is: 1) Assumes network CPC have 200 sensor node, each node of the initial energy set to 0.20 J. 2) Network for the 60 m × 40 m square area, the base station is located in the center of the entire network area. 3) Assuming the position of the nodes in the network information is known. Channel energy loss model parameters shown in Table 1, the network energy consumption, as shown in Fig. 5, shown abscissa denotes said Internetworking a number of rounds, y-coordinate means that the current round of network operation in total energy consumption of energy, the unit is joule. Table 1. Energy loss channel model parameters. Parameter entry Parameter values E elec 60nj / bit fs  2100pj/bit/m mp  40.0013pj/bit/m Fig. 5. The algorithms test results of energy consumption of sensor network. 5. Conclusions This paper proposed water-saving irrigation system based on Internet of things, which has a high degree of automation features. The program is currently developing the water-saving irrigation system has been installed in the domestic use of a vegetable business, the future will continue to improve based on user feedback. Sensors & Transducers Journal, Vol. 16, Special Issue, November 2012, pp. 18-26 26 Acknowledgements This work is partially supported by the Science and Technology Research and Development Program of Shaanxi Province in 2012 #2012K12-03-08, Funding Project for Department of Education of Shaanxi Province in 2012 #12JK0537. Thanks for the help. References [1]. ZigBee specification: ZigBee document 053474r06, Version 1.0, ZigBee Alliance, 2010. [2]. D. Brock, The Physical Markup Language (PML)-A Universal Language for Physical Objects, Technical Report MIT, MIT Auto-ID Center, 2009. [3]. S. Sarma, D. Brock, K. Ashton, The Networked Physical World, White paper MIT, MIT Auto-ID Center, 2011. [4]. B. Warneke, M. Last, B. Liebowitz, and K. Pister, Smart Dust: Communicating with a Cubic-millimeter Computer, IEEE Computer Magazine, 34, 1, 2009, pp. 44-51. [5]. I. F. Akyildig, Weilian Su, Yogesh Sankarasubramaniam, Erdal Cayircy, A Survey on Sensor Networks, IEEE Communications Magazine, 8, 2002, pp. 102-114. [6]. M. Kuorilehto, M. Hannikainen, T. D. Hamalainen. A Survey of Application Distribution in Wireless Sensor Networks, Eurasip Journal on Wireless Communications and Networking, 2005, 5, pp. 774-788. [7]. S. Meguerdichian, F. Koushanfar, M. Potkonjak, M. B. Srivastava, Coverage Problems in Wireless Ad-hoc Sensor Networks, in Proceedings of the IEEE INFOCOM, 3, 2011, pp. 1380-1387. [8]. J. Hightower, G. Boriello, Location Systems for Ubiquitous Computing, Computer, 34, 8, 2001, pp. 57-66. [9]. Y. Shang, W. Ruml, Improved MDS-based Localization, in Proceedings of the IEEE INFOCOM, 2004, pp. 2640-2651. [10]. D. Niculescu, B. Nath. Ad hoc Positioning System (APS) using AOA, in Proceedings of the IEEE INFOCOM, San Francisco, 3, 2003, pp. 1734-1743. [11]. X. Li, Performance study of RSS-based Location Estimation Techniques for Wireless Networks, in Proceedings of the IEEE Military Communication, Conference, 2 October 2005, pp. 1064-1068. [12]. Catovic, Z. Sahinoglu, The Cramer-Rao Bounds of hybrid TOA/RSS and TDOA/RSS Location Estimation Schemes, IEEE Communication Letter, 8, 10, 2009, pp. 626-628. [13]. Silverstein C., Marais H., Henzinger M., Moricz M., Analysis of a very large Web search engine query log. SIGIR Forum, 33, 1, 2008, pp. 6-12. ___________________ 2012 Copyright ©, International Frequency Sensor Association (IFSA). All rights reserved. (http://www.sensorsportal.com) http://www.sensorsportal.com/HTML/Status_of_MEMS_Industry.htm Sensors & Transducers Journal, Vol. 16, Special Issue, November 2012, pp. 27-34 27 SSSeeennnsssooorrrsss &&& TTTrrraaannnsssddduuuccceeerrrsss ISSN 1726-5479 © 2012 by IFSA http://www.sensorsportal.com Design and Simulation of the Air Compressor Control System Yuan He, Gongfa Li, Po Gao, Zehao WU and Cunyuan Li College of Machinery and Automation, Wuhan University of Science and Technology, Hubei, 430081, China Tel.: 086-027-68862283 E-mail: heyuan1230072126.com Received: 11 September 2012 /Accepted: 11 October 2012 /Published: 20 November 2012 Abstract: The air compressor plays an extremely important role in the production of air separation. It provides the required specifications of compressed air for the follow-up air separation processes. The air compressor system is a time-varying, delay and nonlinear complex system, so its design is difficult to achieve the on-site production requirements. Using computer simulation software to simulate the system control program has become an integral part of the process of control system design. Simulink blocks in the Matlab simulation software was used to do modeling and simulation calculation of the air compressor control system. Through the comparisons between the more conventional PID control method and fuzzy self-tuning PID control method in the air compressor, the conclusion can be drawn that the fuzzy self-tuning PID control has a stronger anti-disturbance ability, and it can reach a steady state more easily in the system control. This paper can provide certain theoretical rates to the study of air compressor control system. Copyright © 2012 IFSA. Keywords: Air compressor, Control simulation, Fuzzy intelligent control, Conventional PID control, Fuzzy self-tuning PID control. 1. Introduction The simulation experiment is crucial for the study of control methods. It anal sizes and studies the performance of the control system through the establishment of physical or mathematical models. The simulation of control system has become an integral part of the process of control system design, because simulation is of great importance to the study of control methods. Nowadays, using computer to simulate the control system and research its characteristics has become the main method and way to the study of control methods. Computer simulating ways are convenient, fast and accurate, and they also http://www.sensorsportal.com Sensors & Transducers Journal, Vol. 16, Special Issue, November 2012, pp. 27-34 28 have the advantages of being good at solving large-scale, difficult and uncertain questions of system stimulation. The simulation software can flexibly and effectively analyze and compare different control strategies of the system, and then selects the optimal control result in a large number of control programs. The air compressor control system in air separation production is time-varying, delay and nonlinear, thus its model is more complex and it is difficult to establish a precise mathematical model [1]. On the basis of fuzzy set theory and fuzzy language variables and fuzzy inference logic, fuzzy control combined with expert experience, approximately simulate the human reasoning and decision-making process in the actual production [2]. In this way, the design process of control system will not need to be as accurate as the traditional control systems design mathematical model. Using fuzzy control method to design air compressor controller can overcome the difficulty of establishing a precise mathematical model of the air compressor control system. Matlab is short for Matrix Laboratory, produced by The Math Works, Inc, USA. It is used for algorithm development, data visualization, data analysis, high-level technical computing language of the numeric computation and interactive environment, etc. Simulink module is one of the most important components of the Matlab software [3]. It provides an integrated environment in dynamic system modeling, simulation and comprehensive analysis for scientists. Users can very easily on the computer use Simulink blocks to complete the modeling and simulation of control systems, analysis of the dynamic characteristics of the system. The Simulink blocks in the Matlab simulation software can be used in modeling and simulation of the air compressor control system. Compare the conventional PID control method with fuzzy self-tuning PID control to find out advantages and disadvantages between them, and finally get the optimal program of the air compressor control. 2. Fuzzy Controller Design The controlling mode of large-scale air separation equipment commonly uses the constant pressure control. And in order to ensure safe and stable operation of the equipment, there are some auxiliary controls, such as add/unload control, preventing surge control, interlock protection control and start/stop control. The most important control parameter of air compressor pressure control system is the air compressor outlet pressure. The regulation effect of outlet pressure directly affects the performance and productivity of equipments. Due to the time-varying characteristics, hysteresis and nonlinearity of air compressor system, the article uses the fuzzy control design method was used in designing air compressor pressure fuzzy controller and fuzzy PID controller in article. Fuzzy control is not dependent on precise mathematical control system description. It leverages the operational experience of the workers, establishes of control rules, expresses these rules using computer language, and designs a device to implement these rules. The air compressor system is mainly controlled by the on/off control of the motor and the outlet pressure. When the flow or pressure of the air compressor fluctuates, it maintains the stability of the flow or pressure through the adjustment of speed regulation, the inlet and outlet flow and so on. Speed regulation adjustment has the advantages of the widest adjustment range and the best economical efficiency, but it is not that accurate; while the inlet flow adjustment is simple and has a wide-range control and a better economical efficiency. Different adjustment methods can be chosen according to different processes. No matter which method, the control object is the opening of inlet guide vanes. When using constant pressure control, the opening of inlet guide vane can be adjusted through the adjustment of outlet pressure of the air compressor. And when using constant flow control, it can be achieved through the difference (outlet flow) of outlet pressure of the air compressor. Sensors & Transducers Journal, Vol. 16, Special Issue, November 2012, pp. 27-34 29 Air compressor fuzzy controller used two-dimensional control structure. The controller program structure is shown in Fig. 1. Input is the error comparison between the actual pressure and the pressure set point and error rate of change, output is the inlet guide vane opening amount. The controller input was discreted and done fuzzy dealing. Establish the corresponding relationship between the discredited values and the fuzzy variables, achieve the transformation of exact amount to the fuzzy variables, design and build air compressor pressure control rules. The value of the output U was got through using the fuzzy toolbox of Matlab, and anti-fuzzy method selected the gravity center method of the area. Fig. 1. Fuzzy PID control chart. The input variables have two parameters: the deviation e and the rate of deviation change ec . The output variable has three parameters: pK , iK , dK . The values of three output parameters can be calculated through the fuzzy inference rules. According to the compressor's operating characteristics and on-site operating experience in the operation. The basic domain of the deviation e can be identified as  5.0,5.0  , the discrete domain can be identified as  6,5,4,3,2,1,0,1,2,3,4,5,6 X , the quantization factor of the deviation is 125.06 eK . The basic domain of the change rate of deviation ec can be identified as  3.0,3.0  , the discrete domain can be identified as  6,5,4,3,2,1,0,1,2,3,4,5,6 X , the quantify factor of the change rate of the deviation is 203.06 cK . The incremental pK of the coefficient of proportional link pK , its basic domain can be identified as 2.0,2.0  . The coefficient of the integral part iK , its basic domain can be identified as  1.0,1.0  . The coefficients of the differential link dK , its basic domain can be identified as 1.0,1.0  . Discrete domain of the three output variables both are  6,5,4,3,2,1,0,1,2,3,4,5,6  , then the scale factor of three outputs can be calculated: 03.06/2.01 k ， 02.06.0/1.02 k ， 02.06/1.03 k Shape of the membership function of the three output variables in its variable both ends selected the low resolution Gaussian membership function, selected the high resolution triangular membership function in other places. Sensors & Transducers Journal, Vol. 16, Special Issue, November 2012, pp. 27-34 30 Combined the long-term operational experience of works and expertise's’ experience, building the pK , iK , dK fuzzy control rules [4-5] are shown in the following 49 statements: 1）If ( e is PB ) and ( ec is PB ) then ( pK is NB )( iK is PB )( dK is PB ) 2）If ( e is PB ) and ( ec is PM ) then ( pK is NB )( iK is PB )( dK is PB ) . . . 48）If ( e is NB ) and ( ec is NM ) then ( pK is PB )( iK is NB )( dK is NS ) 49）If ( e is NB ) and ( ec is NB ) then ( pK is PB )( iK is NB )( dK is PS ) 3. Fuzzy Intelligent Control of the Simulation Analysis It is difficult to establish a precise mathematical model for the air compressor system due to its complexity. So the air compressor system was simplified as a second-order system   1 21 1k T s T s  , where 1T , 2T denote constants, and k denotes the amplification. Giving different parameter values to k , 1T and 2T . Then analyzed and compared the simulation results, the optimal control scheme can be found out. When 3k  , 1 1T  , 2 1 4T  , a simulation framework of fuzzy PID control could be established as Fig. 2. Fig. 2. Simulation framework of fuzzy PID control. In Fig. 2, FLC is the rule table of fuzzy control done through off-line design; it can also be obtained by Fuzzy Tool through on-line inference. The two parameters inputted to the controller have the same upper and lower limits, they are 6 and -6, and the upper and lower limits of the control signal are respectively 1 and 0. Integral coefficient iK can eliminate the static error of the system, the input domain of the integrator can be determined, it is [-6, 6]. Order the integral   6 1% 0.06iu t    , iK =0.01. Using synthetical inference mechanism determined the matrix table of the PID fuzzy control, according to the assigned table of fuzzy subset membership degree of E , EC , pK , iK , dK and their control model. When the controller was running online, first it collected the sample signals, then it did the processing, table look-up and operations according to the design, and got the ultimate control quantity, finally it finished the online self-correction of the PID parameters. The parameter values of the three links of the conventional PID can be set by Ziegler-Nichols’ critical Proportioning Method [6] they Sensors & Transducers Journal, Vol. 16, Special Issue, November 2012, pp. 27-34 31 were p