Application of Device Control and Manage Method in Military Barracks

Application of Device Control and Manage Method in Military Barracks

Ping Wan Kaiwen Luo  Shenglin Li 

Department of Information Engineering, Logistical Engineering University, Chongqing 400016, China

Corresponding Author Email:
30 June 2016
| Citation



The intelligent device detection and control system in military barracks is often instrumented with a large number of devices. In order to improve device management level and ensure device operation stabilization, a device control and manage method and its application is proposed in this paper. The proposed method can be broken down into four parts: scheme design, data acquisition, data upload and transfer, applications. The details of each part have been illustrated comprehensively. Finally, a typical application demonstrates that the proposed method can realize the field device controlling and management real time, improve control efficiency and reliability.


Device control, DCMS, DCM, Data acquisition

1. Introduction
2. Scheme Design
3. Data Acquisition
4. Data Upload and Transfer
5. Applications
6. Conclusions

[1] Marie C., Daniel E., Christophe E. and Eric C., “A review of smart homes-Present state and future challenges,” Computer Methods Programs in Biomedicine, vol. 91, pp. 55-81, 2008.

[2] Chhom S., Seo S., Song J. E., Yoon S. H., Kim S. Y. and Cho C. H., “Fractional frequency reuse based adaptive power control scheme for interference litigation in LTE-Advanced cellular network with device-to-device communication,” Lect. Notes Electr. Eng, vol. 363, pp. 429-438, 2016.

[3] Dinh Anh, Tuan Tran, Youming Chen, Minh Q. Chau and Baisong Ning., “A robust online fault detection and diagnosis strategy of centrifugal chiller systems for building energy efficiency,” Energy Buildings, Accepted.

[4] Hasan Ferdowsi., “Model based fault diagnosis and prognosis of nonlinear systems,” Missouri University of Science and Technology, 2013.

[5] Woohyun Kim., “Fault Detection and diagnosis for air conditioners and heat pumps based on virtual sensors,” Purdue University, 2013.

[6] Barakat E., Sinno N. and Keyrouz C., “A remote monitoring system for voltage, current, power and temperature measurement,” Physics Procedia, vol. 55, pp. 421-428, 2014.

[7] Giusepppe M. T. and Alfio D. G., “Remote monitoring system for stand-alone photovoltaic power plants: The case study of a PV-powered outdoor refrigerator,” Energy Conversion and Management, vol. 78, pp. 862-871, 2014.

[8] Deng Xiaogang, Tian Xuemin and Chen Shen., “Modified kernel principal component analysis based on local structure analysis and its application to nonlinear process fault diagnosis,” Chemometrics and Intelligent Laboratory Systems, vol. 127, pp. 195-209, 2013.

[9] Venkat V., Raghunathan R., Kewen Yin and Surya N. Kavuri., “A review of process fault detection and diagnosis part I: Quantitative Model-based methods,” Computers and Chemical Engineering, vol. 27, pp. 293-311, 2003.

[10] Venkat V., raghunathan R., Surya N. K. and Kewen Y., “A review of process fault detection and diagnosis Part III: Process history based methods,” Computers and Chemical Engineering, vol. 27, pp. 327-346, 2003.

[11] Cao L. J., Chua K. S., Chong W. K., Lee H. P. and Gu Q. M., “A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine,” Nueralcomputing, vol. 55, pp. 321-326, 2003.

[12] Shen Yin, Steven X. D., Adel H., Hao Haiyang and Zhang Ping., “A comparion study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process,” Journal of Process Control, vol. 12, pp. 1567-1581, 2012.

[13] Shun Li and Jin Wen., “A model-based fault detection and diagnostic methodology based on PCA method and wavelet transform,” Energy and Buildings, vol. 68, pp. 63-71, 2014.

[14] Michael E. Tipping and Christopher M. Bishop., “Probabilistic principal component analysis,” J. R. Statist. Soc. B, vol. 61, Part 3, pp. 611-622, 1999.

[15] Tao Chen, Elaine Martin and Gary Montague., “Robust probabilistic PCA with missing data and contribution analysis for outlier detection,” Computational Statistics and Data Analysis, vol. 53, pp. 3706-3716, 2009.

[16] Pierrick B., Marc G. and Fabien P., “A low-cost variational-Bayes technique for merging mixture of probabilistic principal component analyzers,” Information Fusion, vol. 14, pp. 268-280, 2013.

[17] Mahmoudreza S., “Sensor fault diagnosis using principal component analysis,” Texus, US: A & M University, 2009.