PPR based similarity search for gas monitoring data

Because of the lack of data sharing mechanism among different safety monitor systems used in current Chinese coal mine enterprises, it is unlikely to fuse and process mine gas monitoring data and human monitoring data. In this article, a mine gas prediction method is proposed based on cloud computing data integrating mode. The method integrates all mine gas monitoring data server resources together, and then processes the obtained data by using network distributed computing resources cluster pattern and analysis the processed data. The analyzed result will provide technique support for decision making. The article introduced the architecture of cloud computing data integrating mode pattern, and built up simulation based on real time data. Simulation result indicated that it is sufficient and accurate to predict mine gas density by using virtual service system to achieve multi-source data calculation.

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Spontaneous reversal of the ‘wall effect’: another possible cause for inaccurate continuous intra-arterial blood gas monitoring data

Anaesthesia ◽

10.1046/j.1365-2044.2003.03207_11.x ◽

2003 ◽

Vol 58 (6) ◽

pp. 605-606

Author(s):

S. Ishikawa ◽

K. Nakazawa ◽

K. Makita

Keyword(s):

Wall Effect ◽

Monitoring Data ◽

Blood Gas ◽

Gas Monitoring ◽

Arterial Blood Gas ◽

Blood Gas Monitoring ◽

Arterial Blood ◽

Spontaneous Reversal ◽

Possible Cause

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Mine Gas Concentration Pre-Warning Based Monitoring Data Relational Analysis

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.634-638.3655 ◽

2013 ◽

Vol 634-638 ◽

pp. 3655-3659 ◽

Cited By ~ 1

Author(s):

Ding Wen Dong ◽

Hong Gang Wang ◽

Peng Tao Jia

Keyword(s):

Monitoring Data ◽

Statistical Characteristics ◽

Gas Monitoring ◽

Gas Concentration ◽

Relational Analysis ◽

Safe Management ◽

Intrinsic Correlation ◽

Mine Gas ◽

Monitoring Data Processing ◽

Grey Relational

For efficient analysis of mine gas monitoring data to expand monitor system function and realize effective gas pre-warning, the gas concentration pre-warning method based on monitoring data processing was studied. Studying by mine gas monitoring data, its statistical characteristics was abstracted, the intrinsic correlation characteristics of time series consisted by gas monitoring data was also analyzed by using grey relational analysis method. Further, the pre-warning indexes and its thresholds were determined, and the gas concentration abnormal situations could be analyzed, which realized timely and dynamically quantitative pre-warning. The case analysis shows that the method has a better applicability for mine-site gas concentration pre-warning, which can offer the effective decision for daily safe management.

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