生成学习系统的混沌与设计

认知学习是一种高度复杂的非线性现象.试图依据生成学习理论和经验学习的思想,建构含有记忆效应的生成学习系统动力学模型,探讨认知学习过程的复杂现象和变化特征,揭示学习系统波动的内生机制和学生认知的混沌规律,并在此基础上提出基于学习混沌的教学系统设计模式,期望能促进学生认知结构的发展.     

Cognitive styles,task presentation mode and mathematical performance

This paper reports the outcomes of an empirical study undertaken to investigate the relationship of prospective teachers’ cognitive styles and levels of performance in measurement and spatial tasks. A total of 116 prospective kindergarten school teachers were tested using the VICS and the extended CSA-WA tests (Peterson 2005) in order to place them along the Verbal/Imagery and the Wholistic/Analytic cognitive style continua. The same prospective teachers were also administered a mathematical test with 6 measurement and 6 spatial tasks. The results suggest that there were no significant differences between Verbalisers-Imagers and Wholistic-Analytic prospective teachers in their performance on the spatial pictorial and textual tasks, and on the measurement textual tasks. However, there were differences between Verbalisers-Imagers and Wholistic-Analytic prospective teachers in their performance on the measurement pictorial tasks. This difference was attributed to the performance of low achievers. High achievers performed in the same way independently of their cognitive styles.     

Interaction of intelligent and autonomous systems – part II: realization of cognitive technical systems

In this study, the application of a meta-modelling technique to structure complex environments is demonstrated with two examples. A special situation-operator model developed to model the human-machine-interaction is applied to automated supervision within the HMI context and to realize flexible and situated interaction of autonomous systems. A concept to automated monitoring of human operators is introduced to illustrate the structuring of complex environments as the first example. In the second example, this modelling technique is applied to a mobile robot to autonomously build and update a mental model of the interaction with the environment as an example of a cognitive technical system.     

用于随机解调器压缩采样的重构判定方法

提出了一种随机解调器压缩采样重构成败的判定方法. 该方法利用两次连续重构所得稀疏信号支撑之间的相关性来判断重构是否成功,其计算复杂度低,易于实现. 仿真结果表明,该方法能准确判断随机解调器压缩采样重构成败,用于宽带频谱感知中能够显著降低信号不稀疏时对主用户的干扰概率. 关键词： 认知无线电 频谱感知 随机解调器 压缩采样     

Math clubs and their potentials: making mathematics fun and exciting. A case study of a math club

The purpose of this study is to describe the results obtained from a survey whose goal was to examine the combination of variables that have contributed to the success of a middle school math club. This is a case of a middle school in which the students are extremely successful in mathematics, and where the majority of the students voluntarily attend its math club. The results of the study show that the students have positive attitudes about mathematics and the club, and that some of the reasons that influenced them to attend the club were those of being with friends and eating donuts at the club. The results were similar for students of both genders and all grades. In addition, since positive attitudes are associated with higher levels of math achievement, such clubs have the potential to encourage students to enrol in additional mathematics classes while in high school, as well as pursuing mathematics related careers.     

Learning Numerosity Representations with Transformers: Number Generation Tasks and Out-of-Distribution Generalization

One of the most rapidly advancing areas of deep learning research aims at creating models that learn to disentangle the latent factors of variation from a data distribution. However, modeling joint probability mass functions is usually prohibitive, which motivates the use of conditional models assuming that some information is given as input. In the domain of numerical cognition, deep learning architectures have successfully demonstrated that approximate numerosity representations can emerge in multi-layer networks that build latent representations of a set of images with a varying number of items. However, existing models have focused on tasks requiring to conditionally estimate numerosity information from a given image. Here, we focus on a set of much more challenging tasks, which require to conditionally generate synthetic images containing a given number of items. We show that attention-based architectures operating at the pixel level can learn to produce well-formed images approximately containing a specific number of items, even when the target numerosity was not present in the training distribution.     

Neural network approach for modeling the performance of reverse osmosis membrane desalting

A neural network-based modeling approach with back-propagation and support vector regression algorithms was investigated as a mean of developing data-driven models for forecasting reverse osmosis (RO) plant performance and for potential use for operational diagnostics. The concept of plant “short-term memory” time-interval was introduced to capture the time-variability of plant performance since both a state of the plant model and standard time-series analyses for both flux decline and salt passage did not result in realistic predictive horizons for practical purposes. Past information of normalized permeate flux and salt passage were introduced as unique input variables along with process operating parameters to capture short-term plant performance variability. Sequential models, where the time-variation within each forecasting time-interval was also taken as input information, and marching forecasting models, where target values were predicted at fixed future times from past plant information, were developed. Models were trained, with normalized permeate flux and salt passage, for various model architectures, memory time-intervals and forecasting times using both back-propagation and support vector regression approaches. State of the plant models (without forecasting) were able to describe the relatively small permeate flux variations but were unable to capture salt passage trends (for any present time condition) since unsteady state phenomena could not be properly described without plant memory information. Forecasting of plant performance, with both sequential and marching models, yielded good predictive accuracy for short-term memory time-intervals in the range of 8–24 h for permeate flux and salt passage for forecasting times up to 24 h. Current work is ongoing to extend the approach for longer time scales and to incorporate data-driven forecasting models of RO plant into control strategies and process diagnostics.     

生物物理学的几个热点领域

物理学与生物科学的交叉由来已久，这不仅解决了自然界许多重大的理论问题，并且在高层次上开辟了新的技术领域，如生物信息学、纳米生物学和脑与认知科学等，文章对当今生物物理学的这几个热点领域进行了介绍。     

Resistance in series model for micellar enhanced ultrafiltration of eosin dye

A study has been performed to quantify the extent of flux decline during micellar enhanced ultrafiltration (MEUF) of an acid dye (eosin red) using hexadecyl (cetyl) pyridinium chloride as the cationic surfactant. Effects of the operating conditions, e.g., transmembrane pressure drop and feed-surfactant-to-dye ratio, on the permeate flux profile and observed retention have been investigated in an unstirred batch ultrafiltration (UF) cell. A simple resistance-in-series model has been used to quantify the flux decline. From the flux decline history, it has been found that the membrane permeability decreases rapidly due to reversible pore blocking and further flux decline is caused by the growth of a gel-type layer over the membrane surface. The different resistances and growth kinetics of the gel layer have been investigated as functions of the operating conditions.     

基于量子遗传算法的认知无线电决策引擎研究

提出了基于量子遗传算法的认知无线电决策引擎，设计了待优化的多目标函数，利用量子遗传算法调整优化无线电参数，运用多载波系统对算法性能进行了仿真分析.实验结果表明该方法在收敛速度、收敛精度和算法稳定性上都明显优于经典遗传算法，在种群规模较小时仍然能获得很好性能，适合于实际实现.不同权重设置模式下仿真结果表明该方法能够在多个目标函数间进行权衡，参数调整结果与当前对目标函数的偏好一致.