Xian-Min Ma’s research while affiliated with Nanjing University of Science and Technology and other places

This article has carried on the analysis in view of the coal mine old communication network and the relay protection installment existence's question, Proposed based on the OMAP platform to build a coal mine power system relay protection program, elaborated the OMAP platform coal mine relay protection principle, To design this equipment's schematic diagram, and has carried on the performance introduction to this equipment's main hardware constitution, this paper has developed the data acquisition function software flow chart, it provided the basis for the development new coal mine distribution network relay protection installment.

Based on fuzzy rough set theory a novel fault diagnosis for the coal mine hoist electrical control system is proposed in the paper. The process of the mine hoist is introduced, and the working principle of the electronic control system is described. The fault types of the coal mine hoist are classified, and the fault sources of different kind of faults are analyzed. The different fault rules are established with fuzzy logical set theory. By rough set theory the fault features are abstracted and the fault rules are simplified. The primary research results show that the suggested method can find the characteristic parameters in the much fault information of the coal mine hoist electrical control system, and it is very helpful to deal with imprecise information of each system fault source signal, and to diagnose fault through the fuzzy rough set theory.

For more accurate network traffic time series prediction, can effectively relieve network overload and network congestion and other phenomena, but the traditional model of network traffic prediction is limited. In this paper, a chaotic time series prediction method of network traffic, first carried out reconstruction of phase space method using mutual information and false near the point method to determine the delay time and embedding dimension, with a small amount of data the maximum Lyapunov index method, thus proving that the network traffic chaotic time series, and establish the corresponding model, made a prediction of its success, the simulation results show that the method has high accuracy.

This article is based on genetic neural network multi-user detection principle in combination with coal mine monitoring multi-channel communication network of information sources, from optimization to solve the optimal maximum likelihood detection start function, for solving the shortcomings of high complexity, given the combination of genetic neural Network multi-user detector unified framework, reducing the amount of computation, and can better track the optimal multi-user detector performance and application in the coal mine monitoring communication networks, to achieve multi-channel flow Qos enhancement and optimization. The simulation results verify the effectiveness of the method. ssion QoS question.

An improved video median noise reduction algorithm is presented for 120 vehicle terminal monitoring system in this paper. The noise causes of the video images in many 120 ambulance vehicle video terminal monitor equipments are analyzed, and the space rigid body model of self-adaptive median noise reduction filter is established to decrease the noises of the video image transmission process. The noise reduction experiment of video images shows that the proposed video median noise reduction algorithm is superior to the traditional adaptive filtering method, because the new method has the superiority of space-time joint noise reduction.

A new method of monitoring the coal level in three coal warehouses is introduced in the paper. Coal bulk position in the coal bin is real-time detected by CCD cameras, and controlled by CAN field bus monitoring system which is composed of ARM7TDMI (-S) and SJA1000 hardware. The principle of monitoring coal level in the coal warehouse is discussed, the position algorithm of the coal level in coal bin is proposed, the system configuration is designed, and the alarm interface is given. The research shows that the coal level monitoring system for the coal warehouse is helpful to raise the real time detection performance and intellectualization of coal position monitor in coal warehouses as well.

Hidden coal gas fire spontaneous combustion troubles are one of the greatest threats to the coal safety production due to the gas explosion. In this paper, a novel method of prediction the hidden fire source is presented by means of choosing the concentration of CO gas as the indicator to detect the hidden coal gas fire location because CO gas is first reach the alarm threshold in the process of coal spontaneous combustion. The chaotic characteristics of CO gas are analyzed, and the duffing chaotic oscillator is used to detect the weak CO gas concentration signals, then the simulation model is established. The research experiment result shows that this method has high sensitivity for predicting the location of coal spontaneous combustion.

An improved C-C algorithm based on the chaotic time series theory to predicate coal gas concentration sudden emission is discussed in this paper. By the improved C-C algorithm the optimization embedded dimension m and the time delay τ are selected to raise correlation integration computing speed in the gas concentration reconstructed phase space. Simulation results show that the coal gas concentration time series has a chaotic characteristic, and the best embedded dimension m and best delay τ are higher accuracy than the traditional C-C algorithm in the dimensional phase space. The results prove that the proposed C-C algorithm to determine the time delays and the embedding dimensions is effective if the time series data are polluted with noises.

The novel method of detecting weak coal gas concentration signal is proposed based on Duffing chaotic oscillator theory in the paper. The chaotic feature of Duffing oscillator is introduced, the basic principle of periodic signal detection with Duffing chaotic oscillator is described, and the chaotic threshold of Duffing oscillator is discussed and determined by the Melnikov function. The autocorrelation theory and chaotic signal amplitude detection method are combined to decrease the detecting errors for weak coal gas concentration signals. Theory analysis and simulation results show that the higher sensitivity and good anti-noise performance are obtained by using of the Duffing chaotic oscillator due to its sensitivity to the initial measurement condition while the tested signal with periodic perturbation is used as the driving force of Duffing equation.

A novel method of the coal gangue identification and classification is introduced, and the coal gangue image process strategy using wavelet transform is proposed to select the coal gangues out from coal bulk on the mining belt transporter in this paper. The identification principle is described, and the automatic selection system is presented, the embedded zero-tree wavelet (EZW) compression coding algorithm is adopted to transfer bit streams of the coal gangue images, the wavelet moment is used to extract the coal gangue image feature. The results of theory analysis and simulation show that the coal gangues are identified and classified from coal bulk with wavelet transform method, which provides the technique foundation for the coal gangue automatic selection system.