A conceptualization of intended learning outcomes supporting self‐regulated learners in indicating learning paths

Intended learning outcomes (ILOs) indicate what learners will be able to achieve after they are taught. Traditionally, ILOs are expressed as plain text or unstructured documents. What if all ILOs of a specific course of study can be conceptualized through a structured diagrammatic technique? It was hypothesized that learners can benefit from this conceptualization in learning, especially in self‐regulated learning. The aim of this study was to investigate whether the ILOs represented in unstructured or structured formats can facilitate learners to identify learning paths. The results revealed that the mean ratings of all learning paths were statistically significantly higher with structured ILOs.     

A model- and simulation-based approach for tolerancing and verifying the functional capability of micro/nano-structured workpieces

The evaluation of functional features of manufactured workpieces is based on GO- and NO-GO-test results, which are obtained by comparing measured geometric characteristics with nominal dimensions and tolerances specified by the designer. These geometrical specifications are based on a tolerancing system, which was originally defined for the function mating capability. Against the background of upcoming lots of other new functions (like reduction of flow resistance, light absorption, reduction of friction, diffraction of light, self-cleaning or mass transmission) are to be realized with our products – particularly by micro- and nano scaled features. If the verification process will deliver the prediction of the achievable degree of functionality, the usability of a part can be assessed more accurately and in consequence quality and economics can be improved. So, a new principle for tolerancing and verifying turns out to be necessary. In this paper the fundamental deficit of the actual tolerancing and specification systems GPS and ASME Y14.5 is derived and the path for enlarging the system by preposing a functional model is shown. To verify the functional capability of the workpieces an approach based on simulations done with the parameterized mathematical–physical model of the function is suggested. Advantages of this approach will be discussed and demonstrated by examples with microstructured inking rolls, crankshafts and injection valves.     

Subsampled terahertz data reconstruction based on spatio-temporal dictionary learning

In this paper, the problem of terahertz pulsed imaging and reconstruction is addressed. It is assumed that an incomplete (subsampled) three dimensional THz data set has been acquired and the aim is to recover all missing samples. A sparsity-inducing approach is proposed for this purpose. First, a simple interpolation is applied to incomplete noisy data. Then, we propose a spatio-temporal dictionary learning method to obtain an appropriate sparse representation of data based on a joint sparse recovery algorithm. Then, using the sparse coefficients and the learned dictionary, the 3D data is effectively denoised by minimizing a simple cost function. We consider two types of terahertz data to evaluate the performance of the proposed approach: THz data acquired for a model sample with clear layered structures (e.g., a T-shape plastic sheet buried in a polythene pellet), and pharmaceutical tablet data (with low spatial resolution). The achieved signal-to-noise-ratio for reconstruction of T-shape data, from only 5% observation was 19 dB. Moreover, the accuracies of obtained thickness and depth measurements for pharmaceutical tablet data after reconstruction from 10% observation were 98.8%, and 99.9%, respectively. These results, along with chemical mapping analysis, presented at the end of this paper, confirm the accuracy of the proposed method.     

Sample re-weighting hyper box classifier for multi-class data classification

In this work, we propose two novel classifiers for multi-class classification problems using mathematical programming optimisation techniques. A hyper box-based classifier (Xu & Papageorgiou, 2009) that iteratively constructs hyper boxes to enclose samples of different classes has been adopted. We firstly propose a new solution procedure that updates the sample weights during each iteration, which tweaks the model to favour those difficult samples in the next iteration and therefore achieves a better final solution. Through a number of real world data classification problems, we demonstrate that the proposed refined classifier results in consistently good classification performance, outperforming the original hyper box classifier and a number of other state-of-the-art classifiers.Furthermore, we introduce a simple data space partition method to reduce the computational cost of the proposed sample re-weighting hyper box classifier. The partition method partitions the original dataset into two disjoint regions, followed by training sample re-weighting hyper box classifier for each region respectively. Through some real world datasets, we demonstrate the data space partition method considerably reduces the computational cost while maintaining the level of prediction accuracies.     

Effect of PEI cathode interlayer on work function and interface resistance of ITO electrode in the inverted polymer solar cells

In this paper, we investigated the effect of PEI cathode interlayer on the work function and the interface resistance of ITO electrode in the inverted polymer solar cells (PSCs) based on PBDTTT-C-T:PC₇₀BM. It is found that a very thin layer of PEI (⩽5.5 nm), either linear PEI (l-PEI) or branched PEI (b-PEI) with different molecular weights, is enough to lower the work function of the ITO electrode and to enhance the photovoltaic performance of the devices. The champion power conversion efficiency (PCE) of the devices with the PEI cathode interlayer is 7.84%, more than doubled of that without the interlayer. However, a thicker PEI interlayer (⩾10 nm) results in abrupt decrease of the PCEs due to the increase of the resistance. Interestingly, for the thicker interlayers, the l-PEI shows high photovoltaic performance than that of b-PEI, which can also be explained by their difference in the resistances. This work supplies an insight into the function of PEI cathode interlayer on improving the work function and resistance of ITO electrode in the inverted PSCs, and provides some instructions on the future design of interlayer materials in PSCs.     

In-situ modification of PEDOT:PSS work function using alkyl alcohols as secondary processing solvents and their impact on merocyanine based bulk heterojunction solar cells

The influence of a series of alkyl alcohols on the work function of PEDOT:PSS thin films is systematically investigated by Kelvin probe measurements. We show that the PEDOT:PSS work function can be increased stepwise from 5.2 eV for pristine PEDOT:PSS to 5.61 eV using either alcohols with different alkyl chain length or varying the amount of alcohol in mixtures with chlorobenzene. Moreover, we demonstrate the effect of work function modification on merocyanine based bulk heterojunction solar cells, resulting in improved values for the open-circuit voltage comparable to those obtained with high work function MoO₃. Thus, the processing method presented herein can potentially serve as a simple, alternative route to adjustable and high work function electrodes while maintaining processability from solution.     

融合分词隐层特征的汉语基本块识别

该文以字为基本标注单位,构建了一种汉语基本块识别的神经网络学习模型。模型联合分词任务的神经网络学习模型与基本块识别任务模型,将分词任务模型中学习得到的隐层特征融入基本块识别的模型中,两模型相互交替优化学习模型参数,并实现了以整句似然函数(而非单字似然函数)作为优化目标的算法。实验结果表明:1)以整句似然函数为优化目标的基本块识别的F值比单字似然情形要高出1.33%,特别是在多字块识别中,其召回率比单字似然情形要高出4.68%;2)融合分词任务模型中的隐层特征的汉语基本块识别模型的结果比不做融合的模型要高出2.17%,说明融合分词隐层特征的交替联合学习方法是有效的。     

基于技术进化趋势的裁剪方法研究

裁剪是TRIZ中一种有效解决问题的方法,其通过删除问题元件,重组系统有用功能进行产品创新设计,达到简化系统与优化产品的目的.为了启发和引导设计者在裁剪过程中有效地利用资源,在使用裁剪规则引导创新设计的基础上,以具有预测性和启发性的技术进化趋势作为知识源,综合分析进化趋势解题与裁剪规则引导功能重组的资源来源,构建了进化趋势与裁剪规则的映射关系,提出了基于裁剪规则的多层次进化趋势选择策略,利用进化趋势辅助功能重组,构建了基于进化趋势的裁剪方法过程模型.最后通过钢带铠装机裁剪实例验证了此方法的合理性.