Crystallization Kinetics of Bi2O3-SiO2 Binary System

The Bi2O3-SiO2 glasses were prepared by the melt cooling method. The non-isothermal crystallization kinetics and phase transformation kinetics of the BS glasses were analyzed by the Kissinger and Augis-Bennett equations by means of differential scanning calorimetry (DSC) and X-ray diffraction (XRD). The results show that three main crystal phases, namely Bi12SiO20, Bi2SiO5, and Bi4Si3O12 are generated sequentially in the heat treatment process. The corresponding activation energy is 150.6, 474.9, and 340.3 kJ/mol. The average crystallization index is 2.5, 2.1, and 2.2. The crystal phases generated by volume nucleation grow in a one-dimensional pattern, and the metastable Bi2SiO5 can be transformed into Bi4Si3O12, which is in a more stable phase

Multidimensional Time Series Fuzzy Association Rules Mining

In this paper, we present a new solution, in which the fuzziness of both subsequences and subsequences interval has been taken into consideration for solving the problem of multidimensional time series fuzzy association rules mining. Aimed at dealing with the new conception, this paper has put forward some key algorithms of the solution. Finally, an application example of multidimensional time series fuzzy association rules mining is illustrated. The result shows that rules with fuzzy interval can only be mined out by the above-mentioned new method

Design and implementation of an intelligent car obstacle avoidance system based on deep learning

Through the integration of deep learning technology, from the simplest driving method to the realizatio n of the “carnetwork road” interaction, the use of STM32F103 microprocessor control chip, and through the PWM technology to achieve the speed and steering gear regulation, at the same time, the use of deep learning self-cognition technology, so that intelligent vehicles can make selfcognitive decisions like human minds , by looking for the best route to avoid some obstacles on the road surface, and the selection of the optimal forecast route, and through the tracking controller to achieve the black line function, through the anti-collision system to achieve the vehicle detection and obstacle avoidance function

Joint Topic-Semantic-aware Social Recommendation for Online Voting

Online voting is an emerging feature in social networks, in which users can express their attitudes toward various issues and show their unique interest. Online voting imposes new challenges on recommendation, because the propagation of votings heavily depends on the structure of social networks as well as the content of votings. In this paper, we investigate how to utilize these two factors in a comprehensive manner when doing voting recommendation. First, due to the fact that existing text mining methods such as topic model and semantic model cannot well process the content of votings that is typically short and ambiguous, we propose a novel Topic-Enhanced Word Embedding (TEWE) method to learn word and document representation by jointly considering their topics and semantics. Then we propose our Joint Topic-Semantic-aware social Matrix Factorization (JTS-MF) model for voting recommendation. JTS-MF model calculates similarity among users and votings by combining their TEWE representation and structural information of social networks, and preserves this topic-semantic-social similarity during matrix factorization. To evaluate the performance of TEWE representation and JTS-MF model, we conduct extensive experiments on real online voting dataset. The results prove the efficacy of our approach against several state-of-the-art baselines.Comment: The 26th ACM International Conference on Information and Knowledge Management (CIKM 2017

Intrinsic energy conversion mechanism via telescopic extension and retraction of concentric carbon nanotubes

The conversion of other forms of energy into mechanical work through the geometrical extension and retraction of nanomaterials has a wide variety of potential applications, including for mimicking biomotors. Here, using molecular dynamic simulations, we demonstrate that there exists an intrinsic energy conversion mechanism between thermal energy and mechanical work in the telescopic motions of double-walled carbon nanotubes (DWCNTs). A DWCNT can inherently convert heat into mechanical work in its telescopic extension process, while convert mechanical energy into heat in its telescopic retraction process. These two processes are thermodynamically reversible. The underlying mechanism for this reversibility is that the entropy changes with the telescopic overlapping length of concentric individual tubes. We find also that the entropy effect enlarges with the decreasing intertube space of DWCNTs. As a result, the spontaneously telescopic motion of a condensed DWCNT can be switched to extrusion by rising the system temperature above a critical value. These findings are important for fundamentally understanding the mechanical behavior of concentric nanotubes, and may have general implications in the application of DWCNTs as linear motors in nanodevices