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.     

A unified model for the structure of polymers in semidilute solution

A model is developed to analyse the concentration dependence of the range ξ_? of the monomer pair correlation function. In semidilute solution, three concentration regimes are found for semiflexible molecules and the crossover points between the various regimes are predicted in terms of the characteristic ratio of the chains in dilute solution and in terms of the Flory interaction parameter χ. A simple physical interpretation is given which explains the concentration dependence of ξ_? based on binary contacts initially and then ternary contacts at higher concentration. Temperature-concentration diagrams are developed for several common polymer-solvent systems.     

A STUDY ON THE SCALING RATE MODEL OF CALCIUM CARBONATE IN SATURATED WATER SYSTEM

1 INTRODUCTIONCalcium carbonate scaling is quite common in chemical processes,especially in heatexchangers.Only limited work has been done in mathematical modelling describingthe calcium carbonate scaling phenomenon.Hasson’s ionic diffusion model happenedto be one being rather broadly accepted.     

基于扩散距离和MDS的非刚性模型相似性分析

在非刚性模型相似性分析方法中, 基于测地距离的等距嵌入方法对模型拓扑变化非常敏感, 对于有局部拓扑变化的完全相似的模型也无法得到正确结果。为了弥补这一不足, 提高非刚性模型相似性分析的准确性, 将扩散距离与多维尺度分析(MDS)相结合, 计算非刚性模型的等距嵌入模型, 再利用迭代最近点(ICP)算法通过计算嵌入模型的相似性来实现原始非刚性模型的相似性分析。实例证明该方法对含有拓扑变化的非刚性模型可以得到理想的相似性分析结果。     

气化炉改用脱油沥青原料系统结垢原因分析及对策

针对气化炉使用脱油沥青过程中出现的结垢问题，重点分析了其原因及机理，介绍了所采取的措施和效果。     

基于特征融合的调制识别增强与迁移演化

针对调制识别中单一图像的特征信息不足,区分度不够高,识别范围受限的问题。本文提出了一种基于时频图和星座图特征融合的调制识别特征增强方法,利用深度学习神经网络提取信号图像的特征,构建特征空间,通过多维特征融合,挖掘和整合不同特征的优势,增强模型算法的鲁棒性。此外运用了模型迁移的方法,仅需对分类器进行训练,大幅节约了训练时间和资源,具有很强的实时性和实用性。仿真结果显示,在0db左右的条件下,相比于单一特征图像,采用特征融合增强的方法能将信号的平均识别率提高约25%,通过模型迁移,省去了卷积神经网络的训练,所需的训练时间约为迁移前的9.6%,消耗内存约为迁移前的7.3%,同时模型的识别率损失控制在了5%以内。     

产业与部门用水关联及尺度效应分析

以河南省为例,应用基于纵向集成的改进假设抽取法,对该区域三次产业和八大部门的水资源消耗及转移状况进行量化分析,由分析结果可知:农业和基础工业是河南省的主要水资源净输出部门,轻工业、高科技工业和建筑业是主要的净输入部门。河南省用水关联性较强产业部门包括农业-轻工业、农业-高科技工业、基础工业-高科技工业以及农业-基础工业。对不同尺度的用水关联成果进行了分析,分析结果表明:不同分析尺度的纵向集成消耗总量是相等的,且与其直接消耗总量相同;用水关联具有非线性的尺度效应,不能通过线性运算实现不同尺度关联效应的转换;分析尺度越小,分类越细化,内部效应越低,系统中流动转移的水量就越多,反之则相反。研究成果可为区域产业结构调整和水资源管理政策的制定提供参考。     

Scaling laws and polystyrene networks: a quasi-elastic light scattering study

Randomly crosslinked networks with a range of crosslink densities were prepared by γ-irradiation. Quasielastic light scattering measurements were made on the gels swollen to equilibrium in cyclohexane at 308, 318 and 333 K. The longitudinal osmotic modulus M_os was obtained from the intensity of the dynamically scattered light, and the cooperative diffusion coefficient D_c calculated from the relaxation rate of the autocorrelation function. The concentration dependence of these parameters at the theta temperature was found to be consistent with the scaling predictions, but at higher temperatures where excluded volume conditions prevail, deviations from scaling behaviour were observed. These discrepancies are probably caused by defects in the network structure.     

渤海莱州湾地下卤水纳滤过程的结垢趋势研究

针对莱州湾地下卤水纳滤过程中纳滤膜表面无机物结垢的问题,利用饱和度法系统研究了碳酸钙、硫酸钙在卤水体系和纳滤过程中的结垢趋势和影响因素。结果表明,在常温下原料卤水中的碳酸钙处于饱和状态,而其中的硫酸钙处于不饱和状态,因此在外部因素诱导下,碳酸钙更容易发生沉淀;降低卤水的pH可以有效抑制碳酸钙结垢;当逐步提高卤水的产水回收率时,纳滤浓水侧卤水中硫酸钙结垢的风险增大,在纳滤精制卤水过程中选择合适的产水回收率可以降低硫酸钙结垢的风险。     

基于密度调整的改进自适应谱聚类算法

针对谱聚类存在构造相似度矩阵时对尺度参数敏感以及处理多重尺度数据集效果不理想的缺陷, 提出一种基于密度调整的改进自适应谱聚类算法. 该算法将样本点所处领域的密度引入谱聚类, 利用密度差来调整样本点之间的相似度, 使其更符合实际簇类中样本点间的内在关系, 在一定程度上解决了多尺度聚类问题; 同时, 通过样本点的近邻距离自适应得到尺度参数, 使算法对尺度参数相对不敏感. 仿真实验验证了所提出算法的有效性和优越性.