Analysis of the physical properties of an in-vessel composting matrix

Efficient compost production requires a thorough understanding of the process dynamics in terms of the feedstock materials used and the interactions of the physical properties involved. The main properties affecting the composting process are temperature, moisture content, bulk density, porosity and oxygen availability. In this study, the correlations between a selected set of physical properties of a batch-composting matrix were determined. The key physical changes in the composting materials for a blend of woodchips, chicken manure and mixed green vegetables have been monitored during a 36-day composting period in a 200-L rotary drum. The daily measurements conducted on the solid samples included temperature, pH, volatile solids, bulk density, moisture content, free airspace and substrates particle density while the carbon dioxide release was monitored weekly using jar respiration tests. The results from the compost process monitoring were a maximum temperature rise to 66.3 °C over the first 3 days, a marked decrease in free airspace from 76.3% to 40.0% at the end of the process, a variation in average composting material particle density from 1097 kg/m³ to 2325 kg/m³, and an increase in wet bulk density from 255 kg/m³ to 628 kg/m³. Correlations developed among free airspace, wet bulk density, dry bulk density and wet moisture content were in agreement with previously determined equations from literature. Free airspace varied linearly with both dry and wet bulk densities (R² value of 0.89 and 0.95, respectively) while the free airspace-wet moisture content profile followed a fourth degree polynomial trend with a correlation coefficient of 0.63.     

Computational Problem Solving in Spatial Substrates -- A Cognitive Systems Engineering Approach

The ability to perform spatial tasks is crucial for everyday life and of great importance to cognitive agents such as humans, animals, and autonomous robots. A common artificial intelligence approach to accomplish spatial tasks is to represent spatial configurations and tasks in form of detailed knowledge about various aspects of space and time. Suitable algorithms then use the representations to compute solutions to spatial problems. In comparison, natural embodied and situated agents often solve spatial tasks without detailed knowledge about geometric, topological, or mechanical laws; they directly relate actions to effects that are due to spatio-temporal affordances in their bodies and environments. Accordingly, we propose a paradigm that makes the spatio-temporal substrate an integral part of the engine that drives spatial problem solving. We argue that spatial and temporal structures in body and environment can substantially support (and even replace) reasoning effort in computational processes: physical manipulation and perception in spatial environments substitute formal computation. While the approach is well known – for example, we employ diagrams as spatial substrate for geometric problem solving and maps for wayfinding – the underlying principle has not been systematically investigated or formally analyzed as a paradigm of cognitive processing. Topology, distance, and orientation constraints are all integrated and interdependent in truly 2- or 3-dimensional space. Exploiting this fact may not only help overcome the need for acquiring detailed knowledge about the interrelationships between different aspects of space; it also can point to a way of avoiding exploding computational complexity that occurs when we deal with these aspects of space in complex real-world scenarios. Our approach employs affordance-based object-level problem solving to complement knowledge-level formal approaches. We will assess strengths and weaknesses of the new cognitive systems paradigm.     

渠首水位实时调控模型——以船行灌区一支渠为例

很多大中型灌区在输配水过程中存在水位衔接不当的问题,加之田面高程存在起伏,使得灌溉时往往存在部分田面不能上水或受淹等问题。本文提出了空间灌溉率的概念,并利用GIS的三维分析功能研究了其与渠道渠首水位之间的关系。分析表明,空间灌溉率与渠首水位之间的关系符合Logistic曲线;针对船行灌区一支渠,得到该Logistic曲线的参数,并确定了一支渠渠首和干渠渠首不同空间灌溉率所需的水位,为灌区水位实时调控提供基础数据。     

State space model predictive fault-tolerant control for batch processes with partial actuator failure

This paper presents a state space model predictive fault-tolerant control scheme for batch processes with unknown disturbances and partial actuator faults. To develop the model predictive fault-tolerant control, the batch process is first treated into a non-minimal representation using state space transformation. The relevant concepts of the corresponding model predictive fault-tolerant control is thus introduced through state space formulation, where improved closed-loop control performance is achieved even with unknown disturbances and actuator faults, because, unlike traditional model predictive fault-tolerant control, the proposed control method can directly regulate the process output/input changes in the design. For performance comparison, a traditional model predictive fault-tolerant control is also designed. Application to injection velocity control shows that the proposed scheme achieve the design objective well with performance improvement.     

遥感器像移与图像参数的分析建模研究

卫星平台运动会产生恶化像质的像移,像移是影响遥感图像质量的重要因素.针对这种情况,建立了像移和图像参数的关系模型,可通过卫星生成图像的内在特征定量研究卫星成像过程中产生的像移,而不必通过产生像移的卫星平台等因素建立物理模型来研究像移.首先模拟了遥感器在轨成像过程,通过人工制造出影响因素的干扰获得了仿真图像数据,然后定量分析了像移与图像参数之间的相关性,选出了相关性较强且适合建模的图像参数,最后通过对这些图像参数构成的向量的降维得到了反映图像质量的综合参数,并建立了综合参数与像移的关系模型,以通过图像综合参数来计算像移.实验结果表明,这种模型能够很好地反映图像综合指标与像移之间的关系.该研究通过图像参数定量研究像移,为在轨运行卫星的遥感器像移与外景条件下的遥感图像要素的相关建模研究提供了部分技术支持.     

不同重力环境的空间机械臂自抗扰轨迹跟踪控制

针对空间机械臂在地面装调与空间应用时,由于重力环境变化导致机械臂模型发生变化的问题,提出了一种自抗扰控制算法,用于完成空间机械臂轨迹跟踪控制的任务.该算法通过将系统模型及未知外扰作为系统的总和扰动,并利用扩张状态观测器对该扰动进行观测且给予补偿,从而提高了系统抗扰的性能.当机械臂模型随重力环境变化而发生变化时,使用同一个自抗扰控制器对其末端轨迹进行控制,均能取得较好的控制效果.通过对系统的稳定性进行分析,证明了所设计控制器的有效性.将仿真结果与PD控制及自适应鲁棒控制做比较,结果表明该控制算法不仅能适应机械臂模型的变化而且还能有效抵抗系统的扰动,从而使系统具有较强的鲁棒性.     

Study on the polymerization of ϵ‐caprolactam in the interlamellar spaces of [TEACOOH]–montmorillonite intercalations complex and its characterization

The polymerization of ?‐caprolactam between the interlamellar spaces of the [TEACOOH]–montmorillonite intercalations complex was attempted using Na–montmorillonite and 10‐carboxy‐n‐decyltriethylammonium bromide to achieve [TEACOOH]–polycaprolactam–montmorillonite, in which montmorillonite (inorganic polymer) is chemically bonded with the polycaprolactam (organic polymer). The results of X‐ray and IR analysis for the samples obtained after polymerization showed that the polymerization reaction has been successfully accomplished. For the purpose of studying the polymeric reaction product more precisely, we have isolated the polymerized product from the silicate layers and analyzed it with X‐ray diffractometer and IR spectrometer. Comparison of the results of X‐ray and IR analysis between the isolated polymer and the polymer that was synthesized by the reaction of ?‐caprolactam only with the organic cation without montmorillonite showed that both obtained polymers are the same compounds. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 88: 1904–1910, 2003     

相空间重构在语音情感识别中的研究

为了更为全面地表征语音情感状态,弥补线性情感特征参数在刻画不同情感类型上的不足,将相空间重构理论引入语音情感识别中来,通过分析不同情感状态下的混沌特征,提取Kolmogorov熵和关联维作为新的情感特征参数,并结合传统语音特征使用支持向量机（SVM）进行语音情感识别。实验结果表明,通过引入混沌参数,与传统物理特征进行识别的方案相比,准确率有了一定的提高,为语音情感的识别提供了一个新的研究途径。     

基于相关向量机的MEMS陀螺仪随机漂移补偿

提出了一种基于相关向量机的MEMS陀螺仪随机漂移预测方法。针对MEMS陀螺仪随机漂移误差的非线性、不确定性等特点,建立了相关向量机预测模型,并采用EM算法获得模型的参数。针对随机漂移的混沌特性,利用相空间重构技术,将重构后的漂移序列作为输入变量进行模型的训练和预测。训练和测试结果均表明,该方法具有很好的预测效果,优于常用的时间序列分析法和支持向量机法。利用预测结果对随机漂移进行补偿,有效地提高了陀螺的使用精度。