In China, horizontal drilling techniques have been widely used in the exploration of coalbed methane (CBM). The drill-in fluids, especially in unconsolidated coal seams, are typically comprised of cellulose polymers, xanthan polymers and guar gums. However, testing and experience have shown that insufficient degradation of filter cakes resulting from even this “clean” drill-in fluids can significantly impede flow capacity at the wellbore wall. Past approaches to minimizing the damage have been the application of strong acids or oxidative breakers systems. They are often only marginally successful, particularly when applied in extended length intervals. Therefore, this paper introduced an engineered technique incorporating non-toxic, environment friendly and economically attractive bio-degradable drilling fluids (BDF).
Extensive lab tests were conducted to evaluate the effects of substrate (polymer) type, substrate concentration, enzyme type and enzyme concentration on the viscosity breaking behaviors of BDFs. We got the conclusions as follows. (1) Power Law model was the best model to matching the rheological properties of BDFs; (2) Compared with konjak, the degradations of Na-CMC and guar gum were easier to be controlled; (3) In the degradation of CMC by enzyme SE-1, the optimal weight concentration ratio of CMC to SE-1 was 3 to 1; (4) Of the three enzymes, enzyme SE-2 had the highest activity and could be used to degrade polymer in shorter time; (5) Higher enzyme concentration could speed up the degradation reaction; (6) Viscosity breaking times fluctuating from 13.5 hours to 74.5 hours and viscosity breaking ratios varying from 20% to 100% could be achieved by modifying the formulations of BDFs.

According to the characteristics of the nonlinear,long time-delays and time-variation in the MSG wastewater treatment system based on three-phase fluidized bed bioreactor(FBBR),amodified T-S model fuzzy adaptive control system based on genetic algorithm(GA)is presented in this paper.In the system,firstly using GA to optimize the membership functions,then reducing the dimension of fuzzy controller and simplifying the rules by an integral unit. Moreover, adopt a prediction method to compensate the time-delay of system, which based on the theory of fuzzy. Finally, the method is verified by experiments.Simulation experimental results show that the method is feasible and effective, which provides an effective approach to solve the problem of process control with long time-delays,large inertia and time-variation.

A new optimization approach, called pheromone, which comes from the collective behavior of ant colonies for food foraging is proposed to optimize task allocation. These ants spread pheromone information and make global information available locally; thus, an ant agent only needs to observe its local environment in order to account for nonlocal concerns in its decisions. This approach has the capacity for task allocation model to automatically find efficient routing paths for processing orders and to reduce communication overhead, which exists in contract net protocol, in shop floor control system. An example confirms that a pheromone-based optimization approach has an excellent allocation performance in shop floor.

This paper proposes a coarse-to-fine two-level synchronous data acquisition and transmission system for binocular stereo vision, which satisfies strict synchronous requirement of stereo vision. Specifically, this synchronization system design contains: coarse level synchronous based on hardware circuit design and the fine level synchronous based on hardware description language (HDL) design. The former includes the synchronization design of clock and external trigger. The latter utilizes a multi-level synchronous control strategy from field-level to pixel-level, which consists of field-synchronous acquisition of the two-channel video inputs, two-channel Ping-pong buffers switch control module, and pixel-synchronous bit-splicing and PCI transmission module. The experiments of synchronous acquisition and display demonstrate the high reliability and great performance of this synchronous system.

Microwave-Induced Thermo-Acoustic Tomograp- phy (MITAT) has attracted more concerns in recent years in biomedical imaging field. It has both the high contrast of the microwave imaging and the high resolution of ultrasound imaging. As compared to optoacoustics, which uses instead a pulsed light for evoking optoacoustic response, thermo-aco- ustic imaging has the advantage of deeper tissue penetration, attaining the potential for wider clinical dissemination, especially for malignant tumors. In this paper, the induced thermo-acoustic wave propagating in a mimic biologic tissue is simulated by numeric method Pseudo-Spectrum Time Domain (PSTD). Due to the excellent performance in noise- depress and the stability for the fluctuation of the model parameters, Time Reversal Mirror (TRM) imaging technique is studied computationally for the simulative received therm- o-acoustic signals. Some thermo-acoustic objects with differ- ent initial pressure distribution are designed and imaged by TRM technique to represent the complex biologic tissue case in a random media. The quality of images generated by TRM technique based on PSTD method hints the potential of the MITAT technique.

The data mining is to discover knowledge from the database, quantitative association rules mining method is difficult for their values are too large. The usual means is dividing quantitative Data to discrete conception. The Cloud model combines fuzziness and randomness organically, so it fits the real world objectively, a new method to mine association rules from quantitative data based on the cloud model was proposed, which first take the original data distribution in the database into account, and then use the trapezoidal cloud model to complicate concepts division, and transforms qualitative data to the quantitative concept, in the conversion take account of the basic characteristics of human behavior fully, divides quantitative Data with trapezium Cloud model to create discreet concepts, the concept cluster within one class, and separated with each other. So the quantitative Data can be transforms to Boolean data well, the Boolean data can be mined by the mature Boolean association rules mining method to find useful knowledge.

This paper studies the design of data fusion algorithm for asynchronous system with integer times sampling. Firstly, the multisensor asynchronous samplings is mapped to the basic axis, accordingly a sampling sequence of single sensor can be taken. Secondly, aiming at the sensor with the densest sampling points, the modified parallel filtering is given. Afterwards, the sequential filtering fusion method is introduced to deal with the case that there are multiple mapped measurements at some sampling point. Finally, a novel parallel filtering fusion algorithm for asynchronous system with integer times sampling is proposed. Besides, a judgment scheme to distinguish measurement number at every sampling point in the fusion period is also designed. One simple computer numerical value simulation is demonstrated to validate the effectiveness of the judgment scheme and the proposed asynchronous fusion algorithm.

Based on averaged cyclic periodogram cyclic spectral density estimating method(ACP), the cyclic spectral features of complex modulated signals are studied and the correspondence with signal parameters is investigated. The feature extraction methods without prior knowledge are developed. Firstly, the expression of complex modulated signals is described and the relationship between signal parameters is given; Secondly, the cyclic spectral features of signals are analyzed using ACP cyclic spectral density estimating method, the features correspondence with signal parameters is obtained; Based on the above, a method for parameter extracting based on cyclic spectral features is proposed. The normalized RMS error (NRMSE) of frank coded and Costas coded signals parameter extraction are measured to verify the validity of the method.