HPAM/有机锆交联体系压裂液的研究

制备了一种HPAM/有机锆交联体系酸性压裂液,分别讨论了HPAM和有机锆交联剂浓度对压裂液成胶性能的影响.结果表明,在25℃下,有机锆交联剂浓度为500mg·L-1,HPAM浓度为500mg·L-1,破胶剂过硫酸铵浓度为16g·L-1.耐矿化度为7 000 mg·L-1,耐温耐剪切测试表明,该压裂液体系在剪切速率170s-1条件下,连续剪切120min,耐温为95℃黏度在300mPa·s以上,表明该压裂液体系具有良好的耐温耐剪切性能.压裂液综合性能评价表明:压裂液体系悬砂性能好,破胶速度快、彻底,破胶液残渣含量低,对岩心伤害小.     

Multi-criteria user selection scheme for learning-based multiuser MIMO cognitive radio networks

For multiuser multiple-input-multiple-output (MIMO) cognitive radio (CR) networks a four-stage transmiision structure is proposed. In learning stage, the learning-based algorithm with low overhead and high flexibility is exploited to estimate the channel state information (CSI) between primary (PR) terminals and CR terminals. By using channel training in the second stage of CR frame, the channels between CR terminals can be achieved. In the third stage, a multi-criteria user selection scheme is proposed to choose the best user set for service. In data transmission stage, the total capacity maximization problem is solved with the interference constraint of PR terminals. Finally, simulation results show that the multi-criteria user selection scheme, which has the ability of changing the weights of criterions, is more flexible than the other three traditional schemes and achieves a tradeoff between user fairness and system performance.     

Hybrid Kalman and unscented Kalman filters for INS/GPS integrated system considering constant lever arm effect

In inertial navigation system(INS) and global positioning system(GPS) integrated system, GPS antennas are usually not located at the same location as the inertial measurement unit(IMU) of the INS, so the lever arm effect exists, which makes the observation equation highly nonlinear. The INS/GPS integration with constant lever arm effect is studied. The position relation of IMU and GPS’s antenna is represented in the earth centered earth fixed frame, while the velocity relation of these two systems is represented in local horizontal frame. Due to the small integration time interval of INS, i.e. 0.1 s in this work, the nonlinearity in the INS error equation is trivial, so the linear INS error model is constructed and addressed by Kalman filter’s prediction step. On the other hand, the high nonlinearity in the observation equation due to lever arm effect is addressed by unscented Kalman filter’s update step to attain higher accuracy and better applicability. Simulation is designed and the performance of the hybrid filter is validated.     

Simulation of coupled THM process in surrounding rock mass of nuclear waste repository in argillaceous formation

To investigate and analyze the thermo-hydro-mechanical(THM) coupling phenomena of a surrounding rock mass in an argillaceous formation, a nuclear waste disposal concept in drifts was represented physically in an in-situ test way. A transversely isotropic model was employed to reproduce the whole test process numerically. Parameters of the rock mass were determined by laboratory and in-situ experiments. Based on the numerical simulation results and in-situ test data, the variation processes of pore water pressure, temperature and deformation of surrounding rock were analyzed. Both the measured data and numerical results reveal that the thermal perturbation is the principal driving force which leads to the variation of pore water pressure and deformations in the surrounding rock. The temperature, pore pressure and deformation of rock mass change rapidly at each initial heating stage with a constant heating power. The temperature field near the heater borehole is relatively steady in the subsequent stages of the heating phase. However, the pore pressure and deformation fields decrease gradually with temperature remaining unchanged condition. It also shows that a transversely isotropic model can reproduce the THM coupling effects generating in the near-field of a nuclear waste repository in an argillaceous formation.     

Forecasting and optimal probabilistic scheduling of surplus gas systems in iron and steel industry

To make full use of the gas resource, stabilize the pipe network pressure, and obtain higher economic benefits in the iron and steel industry, the surplus gas prediction and scheduling models were proposed. Before applying the forecasting techniques, a support vector classifier was first used to classify the data, and then the filtering was used to create separate trend and volatility sequences. After forecasting, the Markov chain transition probability matrix was introduced to adjust the residual. Simulation results using surplus gas data from an iron and steel enterprise demonstrate that the constructed SVC-HP-ENN-LSSVM-MC prediction model prediction is accurate, and that the classification accuracy is high under different conditions. Based on this, the scheduling model was constructed for surplus gas operating, and it has been used to investigate the comprehensive measures for managing the operational probabilistic risk and optimize the economic benefit at various working conditions and implementations. It has extended the concepts of traditional surplus gas dispatching systems, and provides a method for enterprises to determine optimal schedules.     

Performance of cumulant-based rank reduction estimator in presence of unexpected modeling errors

Compared with the rank reduction estimator(RARE) based on second-order statistics(called SOS-RARE), the RARE based on fourth-order cumulants(referred to as FOC-RARE) can handle more sources and restrain the negative impacts of the Gaussian colored noise. However, the unexpected modeling errors appearing in practice are known to significantly degrade the performance of the RARE. Therefore, the direction-of-arrival(DOA) estimation performance of the FOC-RARE is quantitatively derived. The explicit expression for direction-finding(DF) error is derived via the first-order perturbation analysis, and then the theoretical formula for the mean square error(MSE) is given. Simulation results demonstrate the validation of the theoretical analysis and reveal that the FOC-RARE is more robust to the unexpected modeling errors than the SOS-RARE.     

Seismic Behaviour of Beam-Column Joints of Precast and Partial Steel Reinforced Concrete

A beam-column joint of precast and partial steel reinforced concrete( PPSRC) is proposed for precast reinforced concrete frames. The PPSRC consists of partial steel and reinforced concrete. The partial steel is located in the core joint region and the connections between concrete members. This paper presents an experimental study of a series of PPSRC specimens. These specimens are tested under low cyclic loading.Experimental results demonstrate that the bearing capacity of the PPSRC specimens is 3 times that of the ordinary reinforced concrete( RC) beam-column joints. The strength and stiffness degradation rates are slower compared with that of the RC beam-column joints. In addition,the strength of the core joint region and the connections is higher than other parts of the PPSRC specimens. Beam failure occurs firstly for the PPSRC specimens,followed by column failure and connections failure. The failure of the core joint region occurs finally.Test results show that the seismic performance of the PPSRC is better than that of the ordinary RC beam-column joints.     

A Semi-Supervised WLAN Indoor Localization Method Based on 1-Graph Algorithm

For indoor location estimation based on received signal strength (RSS) in wireless local area networks (WLAN), in order to reduce the influence of noise on the positioning accuracy, a large number of RSS should be collected in offline phase. Therefore, collecting training data with positioning information is time consuming which becomes the bottleneck of WLAN indoor localization. In this paper, the traditional semi-supervised learning method based on k-NN and ε-NN graph for reducing collection workload of offline phase are analyzed, and the result shows that the k-NN or ε-NN graph are sensitive to data noise, which limit the performance of semi-supervised learning WLAN indoor localization system. Aiming at the above problem, it proposes a 1-graph-algorithm-based semi-supervised learning (LG-SSL) indoor localization method in which the graph is built by 1-norm algorithm. In our system, it firstly labels the unlabeled data using LG-SSL and labeled data to build the Radio Map in offline training phase, and then uses LG-SSL to estimate user’s location in online phase. Extensive experimental results show that, benefit from the robustness to noise and sparsity of 1-graph, LG-SSL exhibits superior performance by effectively reducing the collection workload in offline phase and improving localization accuracy in online phase.     

PV-SAHP/HP复合系统不同运行模式下系统性能的实验研究

为拓展太阳能的利用方式,减少能源消耗,将光伏-太阳能热泵与热管进行有机结合,提出了光伏-太阳能热泵/热管复合系统的思想;设计并搭建了光伏-太阳能热泵/热管复合系统实验测试平台,对热管单独运行和热泵单独运行模式下系统的性能进行了实验研究.结果表明,在热管单独运行模式下,系统的日平均光热效率低于普通的PV/T系统;而热泵单独运行模式下,系统具有较高的日平均光热效率及热泵的性能系数.     

基于滤波后处理的主动学习高光谱遥感图像分类

有限的样本数据和高维的特征向量使得高光谱图像分类面临巨大挑战.提出一种结合主动学习和滤波器的高光谱遥感图像分类方法.该方法首先选取部分训练样本得到分类模型,然后采用主动学习通过迭代从非训练样本中选择信息量大的样本不断扩大有效样本数,减少了初始训练样本数降低"维数灾难"出现的可能,同时提高了分类器的泛化性能及准确率.通过主动学习和多项逻辑回归分类器对高光谱遥感图像进行初始分类,然后运用滤波器对初始分类结果保边去噪.实验结果表明,本文方法的分类精度高,分类稳定性好.