4-异丁基苯乙酮加氢-羰基化合成布洛芬

采用CuO-ZnO/Al2O3纳米铜基催化剂催化4-异丁基苯乙酮(IBAP)加氢制备1-(4-异丁基苯基)乙醇(IBPE);然后以PdCl2/PPh3/CuCl2为催化体系,将IBPE经羰基化反应合成2-(4-异丁基苯基)丙酸(布洛芬)。研究结果表明:在反应温度80℃,氢气压力1.5MPa,n(H2)∶n(IBAP)=40∶1,液空速0.30h-1的条件下,IBAP的加氢转化率为100%,IBPE收率达到93.6%。以盐酸为酸性介质、丁酮为溶剂,在温度100℃,CO压力5.5 MPa,反应时间24h的条件下,IBPE羰基化合成布洛芬,IBPE的转化率为100%,布洛芬收率达到72.9%。     

遮光罩对GEO对地激光通信系统可通率影响

地球同步卫星(GEO)对地激光通信系统具有通信速率高、保密性好等优势。但系统的光学天线暴露在星体外部,太阳照射对可通率影响严重。针对某GEO对地激光通信系统,不同长度遮光罩对太阳规避造成的可通率及比刚度影响的分析,证明增加遮光罩长度系统可通率提升效果明显。对现有遮光罩形式进行了分析与总结,简述工程中遮光罩长度的确定与限定条件,为优化设计GEO激光通信系统遮光罩及提高系统可通率提供技术基础。     

应变率对上海安山岩强度和变形的影响

为了探究应变率对上海安山岩强度和变形的影响,试验中采用6种不同的应变率(1×10~(-5)s~(-1)~1s~(-1));选取了3种试件尺寸(D=38 mm,L=38 mm;D=38 mm,L=76 mm;D=58 mm,L=116 mm),以此探究试件的尺寸效应;利用微机控制刚性伺服三轴压力试验机对试件进行单轴压缩试验。试验结果表明:存在临界应变率εcr,当应变率低于时,岩石的峰值应力及极限应变随应变率的增大而增大,高于εcr时,则出现下降;试件长径比对εcr产生一定的影响,试件的长径比增加一倍,εcr则增加了一个数量级;当试件的直径一定时,随着长径比的增加,岩石的峰值应力出现了明显的增加,极限应变则明显下降;当长径比一定时,随着尺寸的增加,峰值应力出现了明显的下降;而尺寸对极限应变的影响则较小。     

基于BP神经网络的高精度基波频率检测方法研究

电力系统谐波检测过程对基波频率的检测精度要求越来越高。针对这一需求,在对比了各种常用基波检测方法后,提出一种基于BP(Back Propagation)神经网络的高精度基波频率检测方法。既通过充分有效地学习和训练,构建出了合理的BP神经网络模型,并利用Matlab软件对其进行仿真验证。结果表明,算法对频率测量精度达10-3~10-5,并且任一频率(50 Hz除外)BP神经网络算法的频率测量都比加汉宁窗的FFT算法高2~3个数量级。     

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.