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101.
A viscometer-reactor assembly is used to generate data on the viscosity, η(t), of an example polymerizing system exhibiting the Trommsdorff effect, namely, the bulk free radical polymerization of methyl methacrylate (MMA), at different temperature conditions [near-isothermal and non-isothermal (near-step increase and near-step decrease in temperature)] and at two different initiator, 2,2′-azoisobutyronitrile (AIBN), concentrations. Two types of cup and bob assemblies, viz., the Haake® SV-2 and the Haake® HV-DIN, have been used to measure η(t) of the reaction mass, until reasonably high values of viscosity, well into the gel effect region. Only three sets of experimental data on xm(t), Mw(t) and η(t) under near-isothermal conditions, are used to develop general correlations for the viscosity. These tuned correlations predict the values of the viscosity for a whole variety of other experimental conditions, including non-isothermal cases, reflecting that the physics of the system is well represented by them. Hence, these correlations can be used for other systems after tuning their parameters. The feasibility of on-line soft sensing is demonstrated for a few cases. 相似文献
102.
压缩感知及其图像处理应用研究进展与展望 总被引:2,自引:0,他引:2
压缩感知理论(Compressed sensing,CS)通过少量的线性测量值感知信号的原始结构,并通过求解最优化问题精确地重构原信号.该理论减少了数字图像及视频 获取时的存储及传输代价,也为后续的图像处理及识别的研究提供了新的契机,促进了理论和工程应用的结合. 阐述了CS的基本原理,综述了其关键技术稀疏变换、观测矩阵 设计、重构算法的一系列最新理论成果和发展,深入分析和比较了CS理论应用到图像处理领域的研究和发展状况,总结了其中存在的问题,并对未来的应用前景进行了展望. 相似文献
103.
Wiener模型结构能有效地表征系统的动态和静态特性, 因此这里首先基于这一结构建立软测量模型, 利用动态与静态子模型分别建立辅助变量与主导变量间的动态与静态关系, 并说明该软测量模型的可行性, 给出模型具体表达式. 其次, 针对所提模型, 提出分步辨识方式获得最优模型参数, 说明其可行性. 再次, 为了减少计算和实现模型在线更新, 这里提出参数辨识递推算法, 并给出软测量模型参数的收敛性结论. 通过实例仿真, 可以看出本文提出模型的可行性, 以及分步辨识方式与递推算法的有效性. 相似文献
104.
105.
106.
一种适用于降压DC-DC的改进型电流检测电路 总被引:2,自引:0,他引:2
基于电流模式控制能够较好地改善DC-DC的性能,设计一种适用于降压DC-DC的改进型电流检测电路,所提出的电流检测电路提高了电流感应的速度和精度。通过对电路的理论分析与设计,采用SMIC 0.18μm CMOS工艺模型,利用Cadence工具对电路进行仿真验证,所提出的电流检测电路在负载电流为50 mA~500 mA时都能够达到96%的效率以及小于40 ns的建立时间。在开关频率为2 MHz时,输入电压范围为2.5 V~4.2 V,所需电感值为4.7μH,电容值为10μF,输出电压纹波小于18 mV。 相似文献
107.
《Robotics and Autonomous Systems》2014,62(4):422-435
In recent years, autonomous robots have increasingly been deployed in unknown environments and required to manipulate or categorize unknown objects. In order to cope with these unfamiliar situations, improvements must be made both in sensing technologies and in the capability to autonomously train perception models. In this paper, we explore this problem in the context of tactile surface identification and categorization. Using a highly-discriminant tactile probe based upon large bandwidth, triple axis accelerometer that is sensitive to surface texture and material properties, we demonstrate that unsupervised learning for surface identification with this tactile probe is feasible. To this end, we derived a Bayesian nonparametric approach based on Pitman–Yor processes to model power-law distributions, an extension of our previous work using Dirichlet processes Dallaire et al. (2011). When tested against a large collection of surfaces and without providing the actual number of surfaces, the tactile probe combined with our proposed approach demonstrated near-perfect recognition in many cases and achieved perfect recognition given the right conditions. We consider that our combined improvements demonstrate the feasibility of effective autonomous tactile perception systems. 相似文献
108.
《Journal of Process Control》2014,24(7):1068-1075
This paper developed a new variable selection method for soft sensor applications using the nonnegative garrote (NNG) and artificial neural network (ANN). The proposed method employs the ANN to generate a well-trained network, and then uses the NNG to conduct the accurate shrinkage of input weights of the ANN. This paper took Bayesian information criterion as the model evaluation criterion, and the optimal garrote parameter s was determined by v-fold cross-validation. The performance of the proposed algorithm was compared to existing state-of-art variable selection methods. Two artificial dataset examples and a real industrial application for air separation process were applied to demonstrate the performance of the methods. The experimental results showed that the proposed method presented better model accuracy with fewer variables selected, compared to other state-of-art methods. 相似文献
109.
《Journal of Process Control》2014,24(6):975-990
Soft sensor technology is an important means to estimate important process variables in real-time. Modeling for soft sensor system is the core of this technology. Most nonlinear dynamic modeling methods integrate the processes of building the dynamic and static relationships between secondary and primary variables, which limits the estimation accuracy for primary variables. To avoid the problem, a kind of soft sensor model consisting of a dynamic model in cascade with a static one is proposed. The model identification and update online are conducted in substep way. In order to improve the model update efficiency, two improved Gauss–Newton recursive algorithms, which avoid nonsingular covariance matrix, are proposed for time-invariant and time-variant soft sensor systems. The uniform convergence for dynamic model parameter and the existence of estimation deviations for static model parameters are proved for time-invariant soft sensor system. The parameters of time-variant soft sensor system would be boundedly convergent. Case study confirms that, on the basis of the proposed model and recursive algorithms, the dynamic and static characteristics of soft sensor system can be described efficiently, and the primary variables are ensured to be estimated accurately. 相似文献
110.