A Descriptive Model of Robot Team and the Dynamic Evolution of Robot Team Cooperation

At present, the research on robot team cooperation is still in qualitative analysis phase and lacks the description model that can quantitatively describe the dynamical evolution of team cooperative relationships with constantly changeable task demand in Multi-robot field. First this paper whole and static describes organization model HWROM of robot team, then uses Markov course and Bayesian theorem for reference, dynamical describes the team cooperative relationships building. Finally from cooperative entity layer, ability layer and relative layer we research team formation and cooperative mechanism, and discuss how to optimize relative action sets during the evolution. The dynamic evolution model of robot team and cooperative relationships between robot teams proposed and described in this paper can not only generalize the robot team as a whole, but also depict the dynamic evolving process quantitatively. Users can also make the prediction of the cooperative relationship and the action of the robot team encountering new demands based on this model. Journal web page & a lot of robotic related papers www.ars-journal.co

Novel Genetic Algorithm-Based Evolutionary Support Vector Machine for Optimizing High-Performance Concrete Mixture

An effective method for optimizing high-performance concrete mixtures can significantly benefit the construction industry. However, traditional proportioning methods are not sufficient because of their expensive costs, limitations of use, and inability to address nonlinear relationships among components and concrete properties. Consequently, this research introduces a novel genetic algorithm (GA)–based evolutionary support vector machine (GA-ESIM), which combines the K-means and chaos genetic algorithm (KCGA) with the evolutionary support vector machine inference model (ESIM). This model benefits from both complex input-output mapping in ESIM and global solutions with faster convergence characteristics in KCGA. In total, 1,030 data points from concrete strength experiments are provided to demonstrate the application of GA-ESIM. According to the results, the newly developed model successfully produces the optimal mixture with minimal prediction errors. Furthermore, a graphical user interface is utilized to assist users in performing optimization tasks

Integral and Rxte/Asm Observations on Igr J17098-3628

To probe further the possible nature of the unidentified source IGR J17098-3628, we have carried out a detailed analysis of its long-term time variability as monitored by RXTE/ASM, and of its hard X-ray properties as observed by INTEGRAL. INTEGRAL has monitored this sky region over years and significantly detected IGR J17098-3628 only when the source was in this dubbed active state. In particular, at $\ge$ 20 keV, IBIS/ISGRI caught an outburst in March 2005, lasting for $\sim$5 days with detection significance of 73$\sigma$ (20-40 keV) and with the emission at $<$200 keV. The ASM observations reveal that the soft X-ray lightcurve shows a similar outburst to that detected by INTEGRAL, however the peak of the soft X-ray lightcurve either lags, or is preceded by, the hard X-ray ($>$20 keV) outburst by $\sim$2 days. This resembles the behavior of X-ray novae like XN 1124-683, hence it further suggests a LMXB nature for IGR J17098-3628. While the quality of the ASM data prevents us from drawing any definite conclusions, these discoveries are important clues that, coupled with future observations, will help to resolve the as yet unknown nature of IGR J17098-3628.Comment: 15 pages, 7 figure, accepted in PAS

Angular Reconstruction of a Lead Scintillating-Fiber Sandwiched Electromagnetic Calorimeter

A new method called Neighbor Cell Deposited Energy Ratio (NCDER) is proposed to reconstruct incidence position in a single layer for a 3-dimensional imaging electromagnetic calorimeter (ECAL).This method was applied to reconstruct the ECAL test beam data for the Alpha Magnetic Spectrometer-02 (AMS-02). The results show that this method can achieve an angular resolution of 7.36\pm 0.08 / \sqrt(E) \oplus 0.28 \pm 0.02 degree in the determination of the photons direction, which is much more precise than that obtained with the commonly-adopted Center of Gravity(COG) method (8.4 \pm 0.1 /sqrt(E) \oplus 0.8\pm0.3 degree). Furthermore, since it uses only the properties of electromagnetic showers, this new method could also be used for other type of fine grain sampling calorimeters.Comment: 6 pages, 8 figure

Prediction of permanent deformation in asphalt pavements using a novel symbiotic organisms search-least squares support vector regression

The prediction of asphalt performance can be very important in terms of increasing service life and performance while saving energy and money. In this study, a new hybrid artificial intelligence (AI) system, SOS-LSSVR, has been proposed to predict the permanent deformation potential of asphalt pavement mixtures. SOS-LSSVR utilizes the symbiotic organisms search (SOS) and the least squares support vector regression (LSSVR), which are seen as a complementary system. The prediction model can be established from all input and output data pairs for LSSVR, while SOS optimizes the systems tuning parameters. To avoid sampling bias and to partition the dataset into testing and training, a cross-validation technique was chosen. The results can be compared to those of previous studies and other predictive methods. Through the use of four error indicators, SOS-LSSVR accuracy was verified in predicting the permanent deformation behavior of an asphalt mixture. The present study demonstrates that the proposed AI system is a valuable decision-making tool for road designers. Additionally, the success of SOS-LSSVR in building an accurate prediction model suggests that the proposed self-optimized prediction framework has found an underlying pattern in the current database and thus can potentially be implemented in various disciplines