Ming He^1,2, Hongbin Wang^1,*, Lianke Zhou¹, Pengming Wang³, Andrew Ju⁴

CMC-Computers, Materials & Continua, Vol.57, No.3, pp. 521-532, 2018, DOI:10.32604/cmc.2018.03710

Abstract An important issue for deep learning models is the acquisition of training of data. Without abundant data from a real production environment for training, deep learning models would not be as widely used as they are today. However, the cost of obtaining abundant real-world environment is high, especially for underwater environments. It is more straightforward to simulate data that is closed to that from real environment. In this paper, a simple and easy symmetric learning data augmentation model (SLDAM) is proposed for underwater target radiate-noise data expansion and generation. The SLDAM, taking the optimal classifier of an initial dataset as… More >

Xuefeng Xi¹, Victor S. Sheng^1,2,*, Binqi Sun², Lei Wang¹, Fuyuan Hu¹

CMC-Computers, Materials & Continua, Vol.56, No.2, pp. 185-198, 2018, DOI: 10.3970/cmc.2018.03694

Abstract Multi-target regression is concerned with the simultaneous prediction of multiple continuous target variables based on the same set of input variables. It has received relatively small attention from the Machine Learning community. However, multi-target regression exists in many real-world applications. In this paper we conduct extensive experiments to investigate the performance of three representative multi-target regression learning algorithms (i.e. Multi-Target Stacking (MTS), Random Linear Target Combination (RLTC), and Multi-Objective Random Forest (MORF)), comparing the baseline single-target learning. Our experimental results show that all three multi-target regression learning algorithms do improve the performance of the single-target learning. Among them, MTS performs… More >

E. Lescoute¹, T. De Rességuier¹, J.-M. Chevalier², J. Breil³, P.-H. Maire², G. Schurtz³

CMC-Computers, Materials & Continua, Vol.22, No.3, pp. 219-238, 2011, DOI:10.3970/cmc.2011.022.219

Abstract With the ongoing development of high energy laser facilities designed to achieve inertial confinement fusion, the ability to simulate debris ejection from metallic shells subjected to intense laser irradiation has become a key issue. We present an experimental and numerical study of fragmentation processes generating high velocity ejecta from laser shock-loaded metallic targets (aluminium and gold). Optical transverse shadowgraphy is used to observe and analyze dynamic fragmentation and debris ejection. Experimental results are compared to computations involving a fragmentation model based on a probabilistic description of material tensile strength. A correct overall consistency is obtained. More >