Reduced formulation of a steady fluid-structure interaction problem with parametric coupling

We propose a two-fold approach to model reduction of fluid-structure interaction. The state equations for the fluid are solved with reduced basis methods. These are model reduction methods for parametric partial differential equations using well-chosen snapshot solutions in order to build a set of global basis functions. The other reduction is in terms of the geometric complexity of the moving fluid-structure interface. We use free-form deformations to parameterize the perturbation of the flow channel at rest configuration. As a computational example we consider a steady fluid-structure interaction problem: an incmpressible Stokes flow in a channel that has a flexible wall.Comment: 10 pages, 3 figure

Learning Combinations of Activation Functions

In the last decade, an active area of research has been devoted to design novel activation functions that are able to help deep neural networks to converge, obtaining better performance. The training procedure of these architectures usually involves optimization of the weights of their layers only, while non-linearities are generally pre-specified and their (possible) parameters are usually considered as hyper-parameters to be tuned manually. In this paper, we introduce two approaches to automatically learn different combinations of base activation functions (such as the identity function, ReLU, and tanh) during the training phase. We present a thorough comparison of our novel approaches with well-known architectures (such as LeNet-5, AlexNet, and ResNet-56) on three standard datasets (Fashion-MNIST, CIFAR-10, and ILSVRC-2012), showing substantial improvements in the overall performance, such as an increase in the top-1 accuracy for AlexNet on ILSVRC-2012 of 3.01 percentage points.Comment: 6 pages, 3 figures. Published as a conference paper at ICPR 2018. Code: https://bitbucket.org/francux/learning_combinations_of_activation_function

ANALISIS PERBANDINGAN PREDIKSI KEBANGKRUTAN PERUSAHAAN DENGAN MENGGUNAKAN MULTIVARIATE DISCRIMINANT ANALYSIS DAN REGRESI LOGISTIK PADA PERUSAHAAN PERTAMBANGAN BATUBARA PERIODE 2010-2014

Penelitian ini bertujuan untuk memprediksi kebangkrutan di sub sektor pertambangan yang terdaftar di Bursa Efek Indonesia: menggunakan analisis diskriminan dan regresi logistik periode 2010-2014. Metode sampling yang digunakan dalam penelitian ini adalah purposive sampling. Pengujian hipotesis diuji dengan analisis diskriminan dan analisis regresi logistik untuk mengetahui perbedaan yang signifikan dalam rasio keuangan seperti current, rasio leverage, net profit margin, debt to equity, operating profit margin, total assets turnover untuk membedakan kelompok perusahaan yang dianggap bermasalah dan tidak bermasalah secara statisik pada perusahaan yang terdaftar di bursa efek Indonesia dalam sub sektor pertambangan selama periode 2010-2014. Sumber data penelitian ini berasal dari situs resmi Bursa Efek Indonesia (BEI).Hasil penelitian ini menunjukan tingkat keakuratan penggunaan metode Discriminant Analysis sebesar 80.4% dan Regresi Logistik sebesar 88.2%. Pada metode Discriminant Analysis menunjukkan variabel rasio keuangan yang signifikan adalah leverage ratio dan net profit margin. Sedangkan untuk regresi logistik menunjukkan bahwa yang signifikan yaitu leverage ratio, net profit margin, dan total assets turnover yang dapat mempengaruhi prediksi kebangkrutan perusahaan pada sektor pertambangan batubara periode tahun 2010 sampai dengan 2014