Progressive Crushing of Polymer Matrix Composite Tubular Structures: Review

The present paper reviews crushing process of fibre-reinforced polymer (FRPs) composites tubular structures. Working with anisotropic material requires consideration of specific parameter definition in order to tailor a well-engineered composite structure. These parameters include geometry design, strain rate sensitivity, material properties, laminate design, interlaminar fracture toughness and off-axis loading conditions which are reviewed in this paper to create a comprehensive data base for researchers, engineers and scientists in the field. Each of these parameters influences the structural integrity and progressive crushing behaviour. In this extensive review each of these parameters is introduced, explained and evaluated. Construction of a well-engineered composite structure and triggering mechanism to strain rate sensitivity and testing conditions followed by failure mechanisms are extensively reviewed. Furthermore, this paper has mainly focused on experimental analysis that has been carried out on different types of FRP composites in the past two decades

Predicting Anchor Links between Heterogeneous Social Networks

People usually get involved in multiple social networks to enjoy new services or to fulfill their needs. Many new social networks try to attract users of other existing networks to increase the number of their users. Once a user (called source user) of a social network (called source network) joins a new social network (called target network), a new inter-network link (called anchor link) is formed between the source and target networks. In this paper, we concentrated on predicting the formation of such anchor links between heterogeneous social networks. Unlike conventional link prediction problems in which the formation of a link between two existing users within a single network is predicted, in anchor link prediction, the target user is missing and will be added to the target network once the anchor link is created. To solve this problem, we use meta-paths as a powerful tool for utilizing heterogeneous information in both the source and target networks. To this end, we propose an effective general meta-path-based approach called Connector and Recursive Meta-Paths (CRMP). By using those two different categories of meta-paths, we model different aspects of social factors that may affect a source user to join the target network, resulting in the formation of a new anchor link. Extensive experiments on real-world heterogeneous social networks demonstrate the effectiveness of the proposed method against the recent methods.Comment: To be published in "Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

Correlated Cascades: Compete or Cooperate

In real world social networks, there are multiple cascades which are rarely independent. They usually compete or cooperate with each other. Motivated by the reinforcement theory in sociology we leverage the fact that adoption of a user to any behavior is modeled by the aggregation of behaviors of its neighbors. We use a multidimensional marked Hawkes process to model users product adoption and consequently spread of cascades in social networks. The resulting inference problem is proved to be convex and is solved in parallel by using the barrier method. The advantage of the proposed model is twofold; it models correlated cascades and also learns the latent diffusion network. Experimental results on synthetic and two real datasets gathered from Twitter, URL shortening and music streaming services, illustrate the superior performance of the proposed model over the alternatives

Effect of multi stitched locations on high speed crushing of composite tubular structures

The present paper experimentally investigates progressive energy absorption of fibre-reinforced polymer (FRP) composite tubular structures under high speed loading conditions. Various multi stitched locations are studied to find a correlation between single and multi-locations of stitches and energy absorption capabilities of composite absorbers. The through-thickness reinforcements are applied into locations of 10 mm, 20 mm, 30 mm, 10–20 m, 10–30 mm, 20–30 mm, 10–20–30 mm and 10–15–20–25–30–35 mm from top of the tubes. It is shown that multi-stitched location can cause several increase of crushing load and consequently increase of energy absorption of composite tube absorbers. The idea would be expanded to other designs which are followed by increase of stitched locations and reduction of the distance between stitches to improve the mean force with a smooth and progressive pattern of crushing load

Laminate tailoring of composite tubular structures to improve crashworthiness design at off-axis loading

This paper presents experimental and numerical investigation on the parameters effecting energy absorption capability of composite tubular structures at oblique loading to improve crashworthiness performance. Various inclined angles of 5˚, 10˚, 20˚ and 30˚ were selected for the study of off-axis loading. The results indicate that by increasing the lateral inclination angle the mean crushing force and also energy absorption capability of all tested sections decreased. From design perspective, it is necessary to investigate the parameters effecting this phenomenon. The off-axis loading effect that causes significant reduction in energy absorption was investigated and the effected parameters were improved to increase energy absorption capability. To establish this study, 10˚ off-axis loading was chosen to illustrate the obtained improvement in energy absorption capability. Five cases were studied with combinations of ply-orientation and flat trimming with 45˚ chamfer. This method was applied to the integrated 10˚ off-axis loading and the final results showed significant improvement in energy absorption capability of composite absorbers. Finite element model (FEM) was developed to simulate the crushing process of axial and off-axis composite section in LS-DYNA and the results were in good agreement with the experimental data

Lightweight design to improve crushing behaviour of multi-stitched composite tubular structures under impact loading

This paper presents experimental and numerical studies on the effect of multi-stitching pattern on the energy absorption capability of composite tubular structures under impact loading. A new multi-stitching pattern was developed to study the increase of specific energy absorption capabilities in GFRP and CFRP crash absorbers. The stitching pattern on both specimens showed a significant increase in energy absorption capability under impact loading. According to our results, the specific energy absorption of GFRP and CFRP composite tubes are 17% and 18% higher than non-stitched specimens respectively. A multi-shell finite element model was constructed to predict the axial crushing behaviour and energy absorption capability of composite structures under impact loading. The method is based on an energy-based contact card modelling technique in the stitched and non-stitched area, and the initiation of main central crack growth occurs when the critical separation (PARAM function) is attained, and this represents the functionality of the stitched area during an impact event. The developed numerical approach is efficient in terms of accuracy and simplicity in comparison with the existing methods for multi-layered composites structures

Recurrent Poisson Factorization for Temporal Recommendation

Poisson factorization is a probabilistic model of users and items for recommendation systems, where the so-called implicit consumer data is modeled by a factorized Poisson distribution. There are many variants of Poisson factorization methods who show state-of-the-art performance on real-world recommendation tasks. However, most of them do not explicitly take into account the temporal behavior and the recurrent activities of users which is essential to recommend the right item to the right user at the right time. In this paper, we introduce Recurrent Poisson Factorization (RPF) framework that generalizes the classical PF methods by utilizing a Poisson process for modeling the implicit feedback. RPF treats time as a natural constituent of the model and brings to the table a rich family of time-sensitive factorization models. To elaborate, we instantiate several variants of RPF who are capable of handling dynamic user preferences and item specification (DRPF), modeling the social-aspect of product adoption (SRPF), and capturing the consumption heterogeneity among users and items (HRPF). We also develop a variational algorithm for approximate posterior inference that scales up to massive data sets. Furthermore, we demonstrate RPF's superior performance over many state-of-the-art methods on synthetic dataset, and large scale real-world datasets on music streaming logs, and user-item interactions in M-Commerce platforms.Comment: Submitted to KDD 2017 | Halifax, Nova Scotia - Canada - sigkdd, Codes are available at https://github.com/AHosseini/RP