活性石灰/粉煤灰制备烧结烟气脱硫剂配比试验研究

本文研究了以钢铁企业常用物料活性石灰和自备电厂粉煤灰制备的脱硫剂,在循环流化床脱硫试验装置上进行了烧结烟气脱硫剂配比试验.试验结果表明,活性石灰与粉煤灰的质量比按1:2配置时,脱硫剂钙的利用率较高,具有较好的经济性.     

助卷辊电液伺服系统相关辨识法研究

用相关辨识法分析了卷取机助卷辊电液伺服系统的频率特性 ,采用 Levy法求出系统的传递函数 ,并且给出该系统频率特性相关参数。     

有滞后环节的系统的闭环鲁棒辨识方法

提出一种新的针对带滞后环节的任意阶系统的闭环辨识方法。通过“三步法”，实现在闭环条件下的被控制对象的传递函数参数的辨识，所提出方法具有很强的鲁棒性。仿真例子表明该方法的有效性。     

Identification of a minor component of the sex pheromone ofLeucoptera malifoliella (Lepidoptera,lyonetiidae)

A new minor component in the female volatile extract ofLeucoptera malifoliella (Costa) (Lepidoptera, Lyonetiidae) has been identified as 5,9-dimethyloctadecane (2). The amount detected of the minor compound2 ranged from 4 to 8% in comparison with the major component 5,9-dimethylheptadecane (1). Neither compound has been found in the male volatile extract. The identification has been based on its spectroscopic properties and Chromatographic behavior in comparison with an authentic synthetic sample. The synthesis has been carried out through a short route from 2,6-dimethylcyclohexanone (3). In field tests, compound2 appears to act as a Synergist of the major component1 when mixed with the latter in a 1000.1– 5 ratio.     

Adaptive receding horizon control for constrained MIMO systems

An adaptive control algorithm for open-loop stable, constrained, linear, multiple input multiple output systems is presented. The proposed approach can deal with both input and output constraints, as well as measurement noise and output disturbances. The adaptive controller consists of an iterative set membership identification algorithm, that provides a set of candidate plant models at each time step, and a model predictive controller, that enforces input and output constraints for all the plants inside the model set. The algorithm relies only on the solution of standard convex optimization problems that are guaranteed to be recursively feasible. The experimental results obtained by applying the proposed controller to a quad-tank testbed are presented.     

Variance analysis of identified linear MISO models having spatially correlated inputs,with application to parallel Hammerstein models

This contribution concerns variance analysis of linear multi-input single-output models when the inputs are temporally white but where different inputs may be correlated. An expression is provided for the variance of a linearly parametrized estimate of the frequency response function from one block, i.e. from one input to the output. In particular, this expression reveals that the variance increases in one block when the number of estimated parameters in another block is increased, but levels off when the number of parameters in the other block reaches the number of parameters in the block in question. It also quantifies exactly how correlation between inputs affects the resulting accuracy and a graphical representation is provided for this purpose. The results are applicable to parallel MISO Hammerstein models when the nonlinearities are known and generalize an existing variance expression for this type of model.     

Predicting and interpreting identification errors in military vehicle training using multidimensional scaling

We compared methods for predicting and understanding the source of confusion errors during military vehicle identification training. Participants completed training to identify main battle tanks. They also completed card-sorting and similarity-rating tasks to express their mental representation of resemblance across the set of training items. We expected participants to selectively attend to a subset of vehicle features during these tasks, and we hypothesised that we could predict identification confusion errors based on the outcomes of the card-sort and similarity-rating tasks. Based on card-sorting results, we were able to predict about 45% of observed identification confusions. Based on multidimensional scaling of the similarity-rating data, we could predict more than 80% of identification confusions. These methods also enabled us to infer the dimensions receiving significant attention from each participant. This understanding of mental representation may be crucial in creating personalised training that directs attention to features that are critical for accurate identification.

Practitioner Summary: Participants completed military vehicle identification training and testing, along with card-sorting and similarity-rating tasks. The data enabled us to predict up to 84% of identification confusion errors and to understand the mental representation underlying these errors. These methods have potential to improve training and reduce identification errors leading to fratricide.     

基于Wiener结构的软测量模型及辨识算法

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.     

Semantic weak signal tracing

The weak signal concept according to Ansoff has the aim to advance strategic early warning. It enables to predict the appearance of events in advance that are relevant for an organization. An example is to predict the appearance of a new and relevant technology for a research organization. Existing approaches detect weak signals based on an environmental scanning procedure that considers textual information from the internet. This is because about 80% of all data in the internet are textual information. The texts are processed by a specific clustering approach where clusters that represent weak signals are identified. In contrast to these related approaches, we propose a new methodology that investigates a sequence of clusters measured at successive points in time. This enables to trace the development of weak signals over time and thus, it enables to identify relevant weak signal developments for organization’s decision making in strategic early warning environment.     

从假性紫罗兰酮合成β-紫罗兰酮中主要副产物(I)的分离鉴定

假性紫罗兰酮在浓硫酸及 - 2 0°C低温条件下反应 ,主要生成 β-紫罗兰酮 ,少量的 α-及 γ-紫罗兰酮 ,同时生成副产物 ( I)及 ( II)。用 IR、1H NMR及 MS光谱分析方法 ,对副产物 ( I)进行了结构鉴定 ,结构为 3,4,4a,5 ,8,8a-六氢 - 2 ,5 ,5 ,8a-四甲基 - 2 H - 1 -苯并吡喃