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Abstract. This paper presents an overview of the magnetomechanical modeling of magnetostriction materials. The introductory section starts with phenomenon of magnetostriction, a brief historical development of magnetostrictive materials and a variety of applications. This is followed by subsection on phenomenological constitutive, which is made up of the Zheng-Liu model, the standard square constitutive model and The hyperbolic tangent constitutive equations. The second deal with magnetomechanical behaviors of magnetostrictive materials based on domain rotation, which is made up of Jiles-Atherton model, homogenized energy model and the Armstrong model.
Magnetostriction describes the change in dimensions of a material due to a change in its magnetization, which was first reported by James Prescott Joule in the early 1840s [1] .He observed that iron particles changed their length when their magnetization was changed. Later on, Villari discovered that applying a stress to magnetostrictive materials changes their magnetization, which enables the use of these materials as stress/force sensors. However, magnetostrictive materials were not used as actuators or sensors until much later.Magnetostrictive materials such as Terfenol-D show high strain and blocked stress. In general[2], all magnetostrictives show fast response as magnetic field can be controlled by changing current which can be changed as fast as electric field. Unlike piezoelectric, these materials can be used under static and low frequency conditions with very low impedance but they offer high impedance at high frequencies (kHz range) due to the inductive nature of the materials. Another problem at high frequency is related to formation of eddy current in the material which prevents excitation of the core of the material. Such problems can be overcome by using laminated materials. Most magnetostrictive materials have higher Curie temperature than piezoelectric materials and hence can be operated at higher temperatures.
Moreover, these materials do not need to be poled and hence there is no limitation in applying stress which can cause depoling[3]. The small hysteresis shown by these materials translates to negligible self-heating, and the need for less complicated control algorithms for actuation and precise sensing output. Some of the challenges of using magnetostrictive materials are related to flux leakage and demagnetization effects which demands efficient design of transducer magnetic circuit.
Magnetostriction is the phenomenon of strong coupling between magnetic state and mechanical state of magnetostrictive materials: strains are generated in response to an applied magnetic field, whereas mechanical stresses in the materials produce measurable changes in magnetization. Modeling techniques are vital to magnetostriction materials due to non-linearity, hysteresis and coupling of the materials, which severely limite the device design and applications of magnetostrictive material[3].
This paper presents an overview of the current state of the magnetomechanical modeling of magnetostriction materials. The introductory section starts with phenomenon of magnetostriction,a brief historical note on the development of magnetostrictive materials from nickel alloys (used in the 1940s) to the current research on FeGa alloys and a variety of applications.This is followed by subsection on phenomenological constitutive. The second deal with challenges in magnetomechanical behaviors of magnetostrictive materials based on domain rotation.
2 Phenomenological Constitutive of Magnetostrictive Materials
2.1 The Zheng-Liu Model
In the course of the zheng-Liu modeling[21], the model follow as
0 can be approximated by the hyperbolic tangent function tanh(x), The nonlinear
Fig. 1. Comparison of the calculated magnetization curves with the experimentaldata (Dashed lines: experimental; solid lines: theoretical)
Fig.1 is the predicted magnetic induction intensity curves of the rod at five prestress levels by this new constitutive model. From the comparison of the calculated magnetization curves with the experimental data of an iron rod under various prestresses, it can be found that the predicted curves and experimental curves are consistent qualitatively. Besides a better simulation of the magnetostrictive strain curves and magnetization curves obtained.
2.2 The Standard Square (SS) Constitutive Model
For a one-dimensional problem, the SS constitutive equations become
Fig. 2. Magnetostriction curves (the calculated curves are obtained by use of the SS model)
Fig. 2 that except for the first two cases of pre-stress, the theoretical values from the SS model are in good agreement with the experimental results in the region of the low and moderate magnetic field. However, large differences appear in the high magnetic field region.
3 Magnetomechanical Behaviors of Magnetostrictive Materials
Based
on Domain Rotation
3.1 Jiles-Atherton Model
The Jiles-Atherton model was originally proposed in two papers appearing in 1984 and 1986 with numerous extensions and generalizations appearing in subsequent
literature. The first step of the model construction focuses on characterizing the anhysteretic magnetization an M For 0 = ε , inversion of the direct relation yields
One can employ Boltzmann theory, for ferroelectric materials for ferromagnetic compounds, to derive the Langevin relation
Formulation in terms of the effective field e H yields the anhysteretic model
The energy supplied to the material is then comprised of a conservative component yielding the anhysteretic response and hysteresis losses thus yielding the relation.
Differentiation, and enforcement of the property that domain walls exhibit reversible motion following field reversal until the anhysteretic is achieved, yields the relation
The reversible magnetization Mrev quantifies moment reorientation in response to reversible domain wall bending. this component of the magnetization is modeled by
The total magnetization is given by
Fig. 3. Experimental strain data (– – –) and model solution (——) computed using unfiltered magnetic field data H(t) = nI(t) and a scaled saturation magnetostriction
For comparison purposes, we include in Fig. 3 the corresponding model fits obtained when the filtering function was omitted and an averaged field measurement was used. This was the regime considered in, where it was demonstrated that use of magnetostriction rather than total strain led to an adequate model at low to moderate drive levels but provided inadequate fits at high input levels. These conclusions are reinforced by the model fits observed in Fig.4. It is noted that even when the saturation magnetostriction was scaled to the maximum experimentally observed value, the lack of hysteresis in the relation between and led to an inadequate characterization.
3.2 Homogenized Energy Model
The coupled constitutive relations for the undamped magnetostrictive material are then given by
Fig. 4. (a) Magnetization predi (c) input field H to the model.
To illustrate model prop plotted in Fig. 4(c) was pro tion and strain responses pl were taken to be those sp through a least squares fit minor loop behavior while l of multiply nested minor l
cted by the model, (b) strains profile predicted by the model,
erties under asymmetric minor loop operation, the fi vided as input to the model which yielded the magneti otted in Figs. 4(a) and 4(b). The parameters in the mo ecified in the example in Sec. 3A which were obtai to high drive level data. Loops 1 and 3 illustrate bia oop 2 illustrates the ability of the model to enforce clos oops. Loops 4 and 5 illustrate biased behavior leading and field izaodel ined ased sure g to
saturation. When combined with the experimental results presented in Sec. 3 A, the behavior illustrated here provides the model with substantial flexibility for material characterization and control design.
4 Conclusion
This paper presents an overview of the current state of the magnetomechanical modeling of magnetostriction materials. The introductory section starts with phenomenon of magnetostriction, This is followed by subsection on phenomenological constitutive, which is made up of the Zheng-Liu model , the standard square constitutive model. the predicted curves of the Zheng-Liu model and experimental curves are consistent qualitatively, the standard square constitutive model are in good agreement with the experimental results in the region of the low and moderate magnetic field. the predicted values of the HT model in the high field region are in better agreement with the experimental results than those of the the standard square constitutive model. The second deal with challenges in magnetomechanical behaviors of magnetostrictive materials based on domain rotation, which is made up of Jiles-Atherton Model, Homogenized Energy Model.
Acknowledgements. The authors would like to acknowledge the financial support by the National Natural Science Fund of China (Grant No. 51175395, 50865008, 51165035 and 51161019) , Youth Science Fund of Jiangxi province office of education of China (Grant No. GJJ11247 and GJJ11628), and Youth Science Fund of Nan Chang Institute of Technologe(Grant No. 2012KJ010).
References
1. Atulasimha, J., Flatau, A.B.: A review of magnetostrictive iron–gallium alloys. Smart Materials and Structures 20, 1–15 (2011)
2. Atulasimha, J.: Characterization and modeling of the magnetomechanical behavior of iron–gallium alloys, PhD Dissertation, Department of Aerospace Engineering, University of Maryland, College Park (2006)
3. Datta, S.: Quasi-Static Characterization and Modeling of the Bending Behavior of Single Crystal Galfenol for Magnetostrictive Sensors and Actuators, PhD Dissertation, Department of Aerospace Engineering, University of Maryland, College Park (2009)
4. Clark, A.E., DeSavage, B.F., Bozorth, R.: Anomalous thermal expansion and magnetostriction of single-crystal dysprosium. Phys. Rev. 138, A216–A224 (1965)
Mobile Sensor Data Collecting System Based on Smart Phone
Chen Zhen1 and Gao Qiang2
1 Sino-French Engineer School, Beihang University, Beijing, P.R. China chenzhen0528@ecpk.buaa.edu.cn
2 Department of Electronics and Information Engineering, Beihang University, Beijing, P.R. China gaoqiang@buaa.edu.cn
Abstract. Smart phone applications are widely used with technological development of embedded sensors which bring the functions of sensing, communicating and computing to users. Large sets of sensor data (GPS, Bluetooth, compass, etc.) are exploited to the research of human behaviors and human social activities, but collecting a large scale dataset bears many problems. GPSTracker, a mobile application using embedded GPS module and Bluetooth module to collect mobile and interactive data with server/client architecture, is designed. This data collecting system based on smart phone of Android platform, which gives solutions to major problems of data collecting experiment in the aspects of usability, energy consumption, privacy and incentives for users to keep running it on their device for long-term. The performance analysis after a series of tests shows that GPSTracker is acceptable to users and has a high efficiency of data collection. It’s effective in controlling energy consumption and privacy.
Keywords: Smart Phone, Sensing, Energy Consumption, Privacy.
1 Introduction
With rapid advancement of embedded sensors and smart phones, the smart system which integrated with functions of sensing, computing and communicating has been widely used [1]. By developing a data collecting application, we can capture human mobility and interaction data on demand [2], build a social network model based on mobile data, predict human social relationship and behaviors in the daily life [3], thus promote our quality of life by reducing traffic congestion, limiting spread of infectious diseases, and further improve public resource allocation. Reading different types of sensor data has become available, but for large scale data collection with thousands users during months, we still face many issues [4]:
1. User experience should be considered. The usability and user interface are the keys to ensure that users would choose the software in the first place to provide data.
e Sensor Data Collecting System Based on Smart Phone
2. Energy consumption of decision. Good applicatio
3. User’s privacy must be usage, transmission and p
4. Once users choose to motivation. Developing incentives.
application is an important indicator to influence use n should not affect the overall battery life of the device respected [5]. The application must control and protect ublication of the captured data. use the application, the application must keep use new data collection functions is a way to advance
In this paper we prese deployment of a smart phon System. This application co GPS module; using Bluetoo as human interaction data functions. The rest of thi GPSTracker, provides solut incentives for users, analyze
nt our attempt to realize such a dataset, through e application GPSTracker, which is developed on Andr uld track and record human mobile data using the integra th module to monitor mobile devices near and record th . It could also support future expansion of advan s paper describes the design and accurate functions ions regarding usability, energy consumption, privacy, s the performance of the application and then conclude
2 The GPSTracke
r Application
GPSTracker uses server/cl section we describe the desi
ient architecture to offer data collection services. In gn and main functions of both client and server side.
2.1 Client App Develop
ment
Client side uses construct foreground, main functions
ion as shown in figure 1, consists of background are listed below.
1. Data Collection: the em mobile data and Bluetoo
bedded GPS and Bluetooth modules are used to capt th interaction data.
ers’ e. t the ers’ the the roid ated hese nced s of and es. this and ture
Fig. 1. Client App architecture
2. Database: the database is built by SQLite. Visualize the data by showing records on the UI. Every single record in the data sheet would have unique record ID and unified format. The GPS data includes time, longitude and latitude; Bluetooth data includes time and MAC address of interaction device.
3. Files Encryption: the database files are transformed to text type files, using Java file I/O to read and write data into files stored on SD card. Use AES and RSA algorithm to encrypt the file.
4. UI Functions: the client side could show working status, turn on or off collection data, as shown in figure 2. Records could be displayed on 3 modes of lists: all, today or recent 3 days. User could manage and delete messages with listed options. The user path could be shown on Google Maps screen. In the setting, user could set the method of collection, set when to stop collection of data. Help and explanation are provided on UI. Feedback mode could be chosen, with periodical notification or text message alert.
5. Data Transmission: collected data will be uploaded using GPRS, 3G, LTE or Wi-Fi, while the time of upload is predetermined by the application, although user could change it in the setting. If user chooses not to use daily upload function, a text message alert would then be sent by Server every day.
6. Notification: user could quickly enter application UI via notification. By displaying notification in the android notification center, GPSTracker uses different colors to specify active, mute and sleep mode.
2.2 Server Development
This system offers a central server that has been setup to receive, store, and forward the data. The architecture of the server is shown in figure 3. Data transmission includes compression and encryption of the data. Records of logs are used to record the connection between client and server. Files classification ensures individual data is properly stored using unique ID generated from each device.
Fig. 2. GPSTracker screenshots
3 Solutions to Maj
or Problems
Developers must ensure tha The section below tackles m
t the application is widely accepted and used by end us ain issues GPSTracker confronts when collecting data.
3.1 User Experience
For a better user experien initialization, users only n collection. The sliding mec entry level user to master transmission all run in the b
ce, GPSTracker’s UI is needed to be simple. After eed to operate once in the main UI to configure d hanism to turn on or off the services allows even the m the application quickly. Data collection, storage, ackground, users won’t be bothered by these tedious wor
3.2 Energy Consumpti
When doing continuous d measurements to reduce continuous services. But GP the application offers a se process.
Once the application is i immediately activate. Upon to sleep mode, shut down t again in five minutes. The shutting down rather than co application would not colle
ata collection, some applications use actual progr energy consumption [6] which are effective towa STracker only needs to be used in specific increments, t rvice of automatically controlling runtime of collect
n collection mode, the GPS and Bluetooth module wo the recording of the first set of data, the service would t he GPS and Bluetooth module. The service would turn sensor modules use far less energy during initializing nstant searching for signals. The auto sleep time ensu ct data when the user is asleep during the night.
3.3 Privacy Control
Users could check the data data will be stored in the fo
and delete one piece or all of them in UI list. All Blueto rmat of ten-digit Bluetooth MAC address, so no perso
sers. . the data most and rks. ress ards thus tion ould turn n on and ures ooth onal
Fig. 3. Server architecture
information will be involved with during the interaction. With regarding to data publication, users could choose to authorize the application to use the data or not. During data transmission between mobile devices and server, system would encrypt the data to safeguard users’ privacy. Due to the vulnerability of the android application to decompiling, AES algorithm encryption is not enough. But only using RSA algorithm slows speed to encrypt. So a hybrid method is developed, as shown in figure 4. Using RSA algorithm to generate public key B and C, in which private key C is stored on the server, while public key B is shared with mobile devices. Every time transmission happens, a random 128-digit key is generated as key A, the transmission would then be encrypted by key A and AES. Afterwards, key A encryption is added using key B, file would then be transferred over to server. Server would then use stored key C to deciphering the initial encryption to acquire key A, and then use key A to open the file. The hybrid method ensures both level and speed of encryption.
3.4 Incentives
This paper sorted the way of attracting users into three categories. For majority of the users, game incentive is an essential aspect for the application. For long term research and development, social incentive would seem to be more important.
1. Personal Incentive: by adding Google Maps module, the real time path is shown on UI. This extension provides users with more mobile data recording options such as adding activity information and described location point, thus adding to server’s analysis function, predicting individual movement pattern [7]
2. Game Incentive: a game named Treasure Hunt is added to the new edition. Marking point of interest with clues, users could find or place treasures throughout the map. Treasure data are uploaded to the server for all users to download after updating. Expansion function will set up treasure hunt achievements and scoreboards. With more usage, the application will turn into a social networking game site.
3. Social Incentive: After analysis by server, proximity between users is determined to find potential friends and a social network model is built [8]. The model would suggest a social group where members have the similar activities [9]. These features require more data and extra theoretical research to support the model [10].
Fig. 4. Procedure of data encryption
4 Performance Analysis
GPSTracker was installed in three different Android phones to conduct testing. The test studied twenty students at an average age of 23 had used the application for two weeks; a survey was also conducted at the end of testing cycle. The results are below:
1. As shown in table 1, the application did perform slight differently in different devices, but overall data collection satisfies the benchmark.
Table 1. Data collection performance with 3 devices
2. Table 2 shows that users spent little time on operating and accepted the UI and operation easily, the survey result indicates the application has good usability.
Table 2. Application usability statistic
3. The battery performance is shown in table 3. With GPS and Bluetooth both on service, the devices could satisfy the minimum 12-hour data collection requirement.
Table 3. Energy consumption statistic
4. The hybrid encryption method using both AES and RSA algorithm reaches encryption speed of 300Mbit/s, compared to the traditional speed of 100Mbit/s. It also reduces the risk of privacy concern during transmission of data.
5. According to the survey, 95% of users actively used trace display and Treasure Hunt function; 90% of users looked forward to social network expansion.
5 Conclusion
This paper designed and actualized a smart phone application GPSTracker, using the integrated GPS and Bluetooth module to collect data. This paper discussed the usability, energy consumption and privacy issues and solved them subsequently. It also analyzed the future functionality of the application from personal, game and social incentives. The performance analysis showed that users were generally satisfied. The application supports further research and development, using its extensional functions to attract more users. The collected data will be used towards analyzing human mobility and social issues, building models to predict human behaviors and set up social networks. After full development of the server side, fitting it with analysis and computation function, the real time data analysis and feedback would be a reality.
References
1. Zhiwen, Y., Zhiyong, Y., Xingshe, Z.: Socially aware computing. Chinese Journal of Computers 35(1), 16–26 (2012)
2. Chon, Y., Cha, H.: LifeMap: A smartphone-based context provider for location-based services. IEEE Pervasive Computing 10(2), 58–67 (2011)
3. Yuyao, Y., Fang, Z., Haiyong, L., Ye, T., Xincan, L.: A geolocation-based social network model for mobile Internet. Journal of Computer Research and Development 48(z2), 307–313 (2011)
4. Hossmann, T., Efstratiou, C., Mascolo, C.: Collecting big datasets of human activity one checkin at a time. In: Proceedings of the 4th ACM International Workshop on Hot Topics in Planet-scale Measurement, pp. 15–20. ACM, New York (2012)
5. Raento, M., Oulasvirta, A.: Designing for privacy and self-presentation in social awareness. Personal and Ubiquitous Computing 12(7), 527–542 (2008)
6. Lu, H., Yang, J., Liu, Z., Lane, D., Choudhury, T., Campbell, A.T.: The jigsaw continuous sensing engine for mobile phone applications. In: Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems, pp. 71–84. ACM, New York (2010)
8. Kortuem, G., Segall, Z.: Wearable communities: Augmenting social networks with wearable computers. IEEE Pervasive Computing 2(1), 71–78 (2003)
9. Gonzalez, M.C., Barabasi, A.L.: Complex networks: From data to models. Nature Physics 3(4), 224–225 (2007)
10. Eagle, N., Pentland, A., Lazer, D.: Inferring friendship network structure by using mobile phone data. Proceedings of the National Academy of Sciences 106(36), 15274–15278 (2009)
