Efficient method for aeroelastic tailoring of composite wing to minimize gust response

Aeroelastic tailoring of laminated composite structure demands relatively high computational time especially for dynamic problem. This paper presents an efficient method for aeroelastic dynamic response analysis with significantly reduced computational time. In this method, a relationship is established between the maximum aeroelastic response and quasi-steady deflection of a wing subject to a dynamic loading. Based on this relationship, the time consuming dynamic response can be approximated by a quasi-steady deflection analysis in a large proportion of the optimization process. This method has been applied to the aeroelastic tailoring of a composite wing of a tailless aircraft for minimum gust response. The results have shown that 20%–36% gust response reduction has been achieved for this case. The computational time of the optimization process has been reduced by 90% at the cost of accuracy reduction of 2~4% comparing with the traditional dynamic response analysis

Optimum buckling design of composite stiffened panels using ant colony algorithm

Optimal design of laminated composite stiffened panels of symmetric and balanced layup with different number of T-shape stiffeners is investigated and presented. The stiffened panels are simply supported and subjected to uniform biaxial compressive load. In the optimization for the maximum buckling load without weight penalty, the panel skin and the stiffened laminate stacking sequence, thickness and the height of the stiffeners are chosen as design variables. The optimization is carried out by applying an ant colony algorithm (ACA) with the ply contiguous constraint taken into account. The finite strip method is employed in the buckling analysis of the stiffened panels. The results shows that the buckling load increases dramatically with the number of stiffeners at first, and then has only a small improvement after the number of stiffeners reaches a certain value. An optimal layup of the skin and stiffener laminate has also been obtained by using the ACA. The methods presented in this paper should be applicable to the design of stiffened composite panels in similar loading conditions

Deep Network Approximation: Achieving Arbitrary Accuracy with Fixed Number of Neurons

This paper develops simple feed-forward neural networks that achieve the universal approximation property for all continuous functions with a fixed finite number of neurons. These neural networks are simple because they are designed with a simple and computable continuous activation function $\sigma$ leveraging a triangular-wave function and the softsign function. We prove that $\sigma$-activated networks with width $36d(2d+1)$ and depth $11$ can approximate any continuous function on a $d$-dimensional hypercube within an arbitrarily small error. Hence, for supervised learning and its related regression problems, the hypothesis space generated by these networks with a size not smaller than $36d(2d+1)\times 11$ is dense in the continuous function space $C([a,b]^d)$ and therefore dense in the Lebesgue spaces $L^p([a,b]^d)$ for $p\in [1,\infty)$. Furthermore, classification functions arising from image and signal classification are in the hypothesis space generated by $\sigma$-activated networks with width $36d(2d+1)$ and depth $12$, when there exist pairwise disjoint bounded closed subsets of $\mathbb{R}^d$ such that the samples of the same class are located in the same subset. Finally, we use numerical experimentation to show that replacing the ReLU activation function by ours would improve the experiment results

Learning English Speaking through Mobile-Based Role-Plays: The Exploration of a Mobile English Language Learning App called Engage

[EN] Engage is a new form of mobile application that connects students studying English with teachers in real-time via their smartphones. Students receive target language through preparation dialogues, and then apply it to a role-play with a teacher. The conceptualization and development of Engage follows the user-centred design approach; and the product was built through multiple iterations: in the first iteration, students were invited to try out a paper mock-up; in the second iteration, students tried out a mobile prototype; in the external test, a fully functional application was released to App Store between October 25 and November 20, 2012, and 326 users downloaded it. The application was well-received by these test users, reflected in the post-study survey, student ratings, and students’ usage records. The external tests proved that the technical environment of the application was feasible for production; and the operationalization of the teacher service and cost model were also proven to be feasible and scalable.Yang, B.; Zhou, S.; Ju, W. (2013). Learning English Speaking through Mobile-Based Role-Plays: The Exploration of a Mobile English Language Learning App called Engage. The EuroCALL Review. 21(2):27-38. https://doi.org/10.4995/eurocall.2013.9788OJS2738212Burke, T. & Guest, A. (2010). Using role playing as a teaching strategy: an interdisciplinary approach to learning. Proceedings of the 2nd Annual Conference on Higher Education Pedagogy, 34-35.Buzan, T. (1989). Use both sides of your brain. New York: Penguin.Demouy, V. & Kukulska-Hulme, A. (2010). On the spot: using mobile devices for listening and speaking practice on a French language programme. Open Learning: The Journal of Open, Distance and e-Learning, 25(3), 217-232. https://doi.org/10.1080/02680513.2010.511955Edge, D., Searle, E., Chiu, K., Zhao, J. & Landay, J.A. (2011, May). Micromandarin: mobile language learning in context. 2011 Annual Conference on Human Factors in Computing Systems. Symposium conducted in Vancouver, BC, Canada.Hyerle, D. (2004). Student successes with thinking maps: school-based research, results, and models for achievement using visual tools. CA: Corwin Press. ISO 13407 (1999). Human-centred design processes for interactive systems. London: British Standards Institution.Karat, C. (1997). Cost-justifying usability engineering in the software life cycle. In M. Helander, T.K.Landauer and P.Prabhu (Eds.), Handbook of Human-Computer Interaction (pp. 653-688). Amsterdam: Elsevier. https://doi.org/10.1016/B978-044481862-1.50098-4Kondo, M., Ishikawa, Y., Smith,C., Sakamoto, K., Shimomura, H., and Wada,N. (2012). Mobile assisted language learning in university EFL courses in Japan: developing attitudes and skills for selfregulated learning. ReCALL, 24, 169187. https://doi.org/10.1017/S0958344012000055Kukulska-Hulme, A. and Shield, L.(2008). An overview of mobile assisted language learning: from content delivery to supported collaboration and interaction. ReCALL, 20(3), 271-289. https://doi.org/10.1017/S0958344008000335Kujala,S. (2003). User involvement: a review of the benefits and challenges. Behavior & Information Technology, 22(1),1-16. https://doi.org/10.1080/01449290301782Liu, T.-Y. (2009). A context-aware ubiquitous learning environment for language listening and speaking. Journal of Computer Assisted Learning, 25(6), 515-527. https://doi.org/10.1111/j.1365-2729.2009.00329.xMiangah, T. M., and Nezarat, A. (2012). Mobile-assisted language learning. Journal of Distributed and Parallel Systems, 3(1), 309-319. https://doi.org/10.5121/ijdps.2012.3126Parrish, B. (2004). Teaching adult ESL: a practical introduction. New York: McGraw-Hill Companies.Rubin, J. (1994). Handbook of usability testing: how to plan, design, and conduct effective tests. New York: Wiley.Schafer, R. W. (1994). Scientific Bases of Human-Machine Communication by Voice. In D.B. Roe (Eds.), Voice communication between humans and machines(pp.34-75). Washington, D.C.: National Academy Press.Senf, M. (2012, Dec). Role-play, simulations and drama activities. DocumBase. Retrieved from http://en.convdocs.org/docs/index-44311.htmlSnyder, C. (2003). Paper prototyping: the fast and easy way to design and refine user interfaces. San Diego, CA: Morgan Kaufmann Pub.Sousa, D. A. (2006). How the brain learns. CA: Corwin Press.Traxler, J. (2007). Current state of mobile learning. International Review on Research in Open and Distance Learning, 8(2), 9-24

Three-body problem -- from Newton to supercomputer plus machine learning

The famous three-body problem can be traced back to Newton in 1687, but quite few families of periodic orbits were found in 300 years thereafter. As proved by Poincar\`{e}, the first integral does not exist for three-body systems, which implies that numerical approach had to be used in general. In this paper, we propose an effective approach and roadmap to numerically gain planar periodic orbits of three-body systems with arbitrary masses by means of machine learning based on an artificial neural network (ANN) model. Given any a known periodic orbit as a starting point, this approach can provide more and more periodic orbits (of the same family name) with variable masses, while the mass domain having periodic orbits becomes larger and larger, and the ANN model becomes wiser and wiser. Finally we have an ANN model trained by means of all obtained periodic orbits of the same family, which provides a convenient way to give accurate enough predictions of periodic orbits with arbitrary masses for physicists and astronomers. It suggests that the high-performance computer and artificial intelligence (including machine learning) should be the key to gain periodic orbits of the famous three-body problem.Comment: 31 pages, 6 figures, 2 tables, https://www.researchsquare.com/article/rs-395522/v