Structural health monitoring system based on multi-agent coordination and fusion for large structure

In practical applications of structural health monitoring technology, a large number of distributed sensors are usually adopted to monitor the big dimension structures and different kinds of damage. The monitored structures are usually divided into different sub-structures and monitored by different sensor sets. Under this situation, how to manage the distributed sensor set and fuse different methods to obtain a fast and accurate evaluation result is an important problem to be addressed deeply. In the paper, a multi-agent fusion and coordination system is presented to deal with the damage identification for the strain distribution and joint failure in the large structure. Firstly, the monitoring system is adopted to distributedly monitor two kinds of damages, and it self-judges whether the static load happens in the monitored sub-region, and focuses on the static load on the sub-region boundary to obtain the sensor network information with blackboard model. Then, the improved contract net protocol is used to dynamically distribute the damage evaluation module for monitoring two kinds of damage uninterruptedly. Lastly, a reliable assessment for the whole structure is given by combing various heterogeneous classifiers strengths with voting-based fusion. The proposed multi-agent system is illustrated through a large aerospace aluminum plate structure experiment. The result shows that the method can significantly improve the monitoring performance for the large-scale structure.     

Supporting primary students' learning of fraction conceptual knowledge through digital games

With the advent of mobile technologies, well-designed fraction apps can be used to help children gain fraction knowledge, a challenging topic for both teachers and students. The present pilot study adopted a quasi-experimental design to investigate whether children can learn fraction concepts equally well if half of the lesson time (20 min) is replaced with game-based learning. Keeping the total lesson time (40 min) identical, the control group (N = 33) received traditional instruction, and the experimental group (N = 32) was presented with a blended learning approach spending half of the class time (20 min) playing tablet-based fraction games, where each of the learners had their own tablet. The results suggested that in the posttest, the experimental group achieved similar learning gains to the control group and appear to have achieved better performance in the transfer test than the control group. This paper also discusses the efficiency of game-based learning, the mechanism of how fraction games might enhance learning, and the potential of integrating game-based learning in educational settings.     

Evolutionary collective behavior decomposition model for time series data mining

In this research, we propose a novel framework referred to as collective game behavior decomposition where complex collective behavior is assumed to be generated by aggregation of several groups of agents following different strategies and complexity emerges from collaboration and competition of individuals. The strategy of an agent is modeled by certain simple game theory models with limited information. Genetic algorithms are used to obtain the optimal collective behavior decomposition based on history data. The trained model can be used for collective behavior prediction. For modeling individual behavior, two simple games, the minority game and mixed game are investigated in experiments on the real-world stock prices and foreign-exchange rate. Experimental results are presented to show the effectiveness of the new proposed model.     

Copper–4,4′-dipyridyl coordination compound as solid reagent for spectrophotometric determination of reducing sugar employing a multicommutation approach

In this work a multicommuted flow system employing copper–4,4′- dipyridyl coordination compound as the solid-phase reagent for the spectrophotometric determination of reducing sugar was developed. The coordination compound was synthesized through a reaction of the 4,4′-dipyridyl and copper (II) nitrate, under hydrothermal conditions. The complex was characterized by infrared spectroscopy (FTIR), power X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive spectrometer (EDS) and thermogravimetric analysis (TGA). Based on the characterization, a multicommuted spectrophotometric procedure for the determination of reducing sugar using copper (II) complex as solid reagent is proposed. The proposed method was based on the redox reaction between a monosaccharide, such as fructose and glucose (reducing sugar) and Cu(II). This reaction, mediated in an alkaline medium, produces a yellow compound that can be determined by absorption electronic spectroscopy (λ_ABS = 420 nm). Under optimum experimental conditions, a linear response ranging from 1.0 to 20.0 g L⁻¹ (R = 0.9978 and n = 5), a detection (3σ criterion) and quantification (10σ criterion) limit estimated at 0.23 and 0.75 g L⁻¹, respectively, a standard deviation relative of 4.7% (n = 7), for a reference solution of 10.0 g L⁻¹ reducing sugar, and a sampling rate of 75 determinations per hour were achieved. The proposed system was applied to the determination of reducing sugars in coconut water and juices. The analysis of ten samples and the application of the t-test to the results found, and those obtained using reference procedures (AOAC), provided no significant differences at a 95% confidence level. This system enabled the analysis of reducing sugar with ease and simplicity, providing a significant economy of the solid reagent (600 μg per determination) and reducing effluent generation.     

Hierarchical Ni-BDC coated FeOOH nanosheets: A coordination tuning synergistic electrocatalyst with enhanced activity for water oxidation

Hierarchical composites represent a class of efficient electrocatalysts for renewable energy storage and conversion technologies owing to the porous structure and additional exposure of metal sites. Herein, a Ni-based metal organic frameworks (MOFs) (marked as Ni-BDC, BDC stands for 1,4-benzenedicarboxylic acid) nanosheet is successfully fabricated on hydroxyl iron oxide (FeOOH) array with carbon fiber cloth (CFC) as substrate. Benefit from the coordination tuning synergistic effect of the distinct chemical composition and the hierarchical structure for fast mass transportation, the as-obtained FeOOH@Ni-BDC illustrates excellent catalytic ability for electrochemical water oxidation with low overpotential of 270 mV to reach 10 mA/cm² current and good durability in alkaline electrolyte. The novelty of this work lies in the modulation of electronic structure of the FeOOH with Ni-BDC through coordination effect to enhance the activity of the hierarchical composite electrocatalyst. This work is expected to guide the preparation of efficient electrocatalyst for new type alternative energy sources exploitation in near future.     

运动会新闻发布系统的设计与实现

针对运动会比赛期间随时需要方便、快捷、高效地更新大量新闻的需求 ,研制和开发一套运动会新闻发布系统。利用微软最新推出的asp .net动态网页开发工具 ,采用web分布式三层设计模型设计实现了一套操作简单、功能完善、具有通用性的运动会新闻发布系统     

Measuring College Students' Motives for Playing Drinking Games.

Students may choose to play drinking games not only for reasons related to alcohol consumption but also because of incentives related to other aspects of play (competition, fun, interpersonal dominance, etc.). College students (120 men and 167 women) completed measures of motives for playing (based on T. J. Johnson, S. Hamilton, & V. L. Sheets, 1999) and consequences of playing drinking games. Exploratory principal-components analysis identified 8 reasons for playing. Men and women differed in their endorsement of the factors. Motives for play directly predicted consequences of play independently of alcohol consumption. Specific motives predicted specific types of consequences. In multiple regression analyses, Conformity motives were negatively related to consequences and may represent a form of protective motive. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Artificial neural networks for coordination and control: The portability of experiential representations

It is time to locate connectionist representation theory in the new wave of robotics research. The utility of representations developed in artificial neural networks (ANNs) during learning has been demonstrated in cognitive science research since the 1980s. The research reported here puts learned representations to work in a decentered control task, the disembodied arm problem, in which a mobile robot operates an arm fixed to a table to pick up objects. There is no physical linkage between the arm and the robot and so the robot's point of view must be decentered. This is done by developing a modular Artificial Neural Net system in three stages: (i) a classifier net is trained with laser scan data to output transformationally invariant position classes; (ii) an arm net is trained for picking up objects; (iii) an inter net is trained to communicate and coordinate the sensing and acting. The completed system is shown to create new nonsymbolic transformationally invariant representations in order to perform the effective generalization of decentered viewpoints.     

不完全信息下的最终报价仲裁

基于争议双地仲裁人的裁决值有不同的概率估计，而这些估计是双方共同知识这一前提分析最终报价中仲裁，把它构造成一个不完全信息的非零和对策模型。然后给出了局部、全局Ｎａｓｈ平衡报价策略存在唯一的充分条件，最后探讨了双方认识存在的差异对争议人报价行为的影响。     

Neural networks for contract bridge bidding

The objective of this study is to explore the possibility of capturing the reasoning process used in bidding a hand in a bridge game by an artificial neural network. We show that a multilayer feedforward neural network can be trained to learn to make an opening bid with a new hand. The game of bridge, like many other games used in artificial intelligence, can easily be represented in a machine. But, unlike most games used in artificial intelligence, bridge uses subtle reasoning over and above the agreed conventional system, to make a bid from the pattern of a given hand. Although it is difficult for a player to spell out the precise reasoning process he uses, we find that a neural network can indeed capture it. We demonstrate the results for the case of one-level opening bids, and discuss the need for a hierarchical architecture to deal with bids at all levels.