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Linghui Kong, Haizhong Qian, Yuqing Wu, Xinyu Niu, Di Wang and Zhekun Huang
Building outlines are important for emergency response, urban planning, and change analysis and can be quickly extracted from remote sensing images and raster maps using deep learning technology. However, such building outlines often have irregular bound...
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Guoqing Chen and Haizhong Qian
With the increasing availability of remote sensing images, the regularization of jagged building outlines extracted from high-resolution remote sensing images has become a current research hotspot. Based on an existing method proposed earlier by this aut...
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Yi Wang, Yating Xu, Tianjian Li, Tao Zhang and Jian Zou
Image deblurring based on sparse regularization has garnered significant attention, but there are still certain limitations that need to be addressed. For instance, convex sparse regularization tends to exhibit biased estimation, which can adversely impa...
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Olga Kostyukova and Tatiana Tchemisova
In this paper, we continue an earlier study of the regularization procedures of linear copositive problems and present new algorithms that can be considered as modifications of the algorithm described in our previous publication, which is based on the co...
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Xin Qiao, Yoshikazu Kobayashi, Kenichi Oda and Katsuya Nakamura
This study developed a novel acoustic emission (AE) tomography algorithm for non-destructive testing (NDT) based on Lasso regression (LASSO). The conventional AE tomography method takes considerable measurement data to obtain the elastic velocity distrib...
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Sangkyun Lee and Jeonghyun Lee
Deep neural networks (DNNs) have been quite successful in solving many complex learning problems. However, DNNs tend to have a large number of learning parameters, leading to a large memory and computation requirement. In this paper, we propose a model c...
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M. R. T. Arruda, M. Trombini and A. Pagani
This study examines a new approach to facilitate the convergence of upcoming user-subroutines UMAT when the secant material matrix is applied rather than the conventional tangent (also known as Jacobian) material matrix. This algorithm makes use of the v...
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Lei Yu, Xuewei Zhang and Yan Chu
In this paper, an adaptive dual-regularization super-resolution reconstruction algorithm based on sub-pixel convolution (MPSR) is proposed. There are two novel features of the algorithm: First, the traditional regularization algorithm and sub-pixel convo...
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George Tzougas and Konstantin Kutzkov
We developed a methodology for the neural network boosting of logistic regression aimed at learning an additional model structure from the data. In particular, we constructed two classes of neural network-based models: shallow?dense neural networks with ...
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Rong Zhao, Cheng Du, Jianyong Zhang, Ruixue Cheng, Zhongqiang Yu and Bin Zhou
Laser absorption spectroscopy tomography is an effective combustion diagnostic method for obtaining simultaneous two-dimensional distribution measurements of temperature and gas molar concentrations. For the reconstruction process of complex combustion f...
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Florin Ilarion Miertoiu and Bogdan Dumitrescu
In this paper, the Feasibility Pump is adapted for the problem of sparse representations of signals affected by Gaussian noise. This adaptation is tested and then compared to Orthogonal Matching Pursuit (OMP) and the Fast Iterative Shrinkage-Thresholding...
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Paulo Vitor de Campos Souza, Augusto Junio Guimaraes, Vanessa Souza Araújo, Thiago Silva Rezende, Vinicius Jonathan Silva Araújo
Pág. 114 - 133
This paper presents a novel learning algorithm for fuzzy logic neuron based on neural networks and fuzzy systems able to generate accurate and transparent models. The learning algorithm is based on ideas from Extreme Learning Machine [36], to achieve a l...
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Fan Lin, Yingpin Chen, Yuqun Chen and Fei Yu
Image deblurring under the background of impulse noise is a typically ill-posed inverse problem which attracted great attention in the fields of image processing and computer vision. The fast total variation deconvolution (FTVd) algorithm proved to be an...
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Kemal Güven and Andaç Töre Samiloglu
Neural networks are one of the methods used in system identification problems. In this study, a NARX network with a serial-parallel structure was used to identify an unknown aerial delivery system with a ram-air parachute. The dataset was created using t...
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Kun Yu, Yufeng Cheng, Longfei Li, Kaihua Zhang, Yanlei Liu and Yufang Liu
Underwater image restoration is a challenging problem because light is attenuated by absorption and scattering in water, which can degrade the underwater image. To restore the underwater image and improve its contrast and color saturation, a novel algori...
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Hans-Georg Raumer, Daniel Ernst and Carsten Spehr
In the field of aeroacoustic source imaging, one seeks to reconstruct acoustic source powers from microphone array measurements. For most setups, one cannot expect a perfect reconstruction. The main effects that contribute to this reconstruction error ar...
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Gui-Rong You, Yeou-Ren Shiue, Wei-Chang Yeh, Xi-Li Chen and Chih-Ming Chen
In ensemble learning, accuracy and diversity are the main factors affecting its performance. In previous studies, diversity was regarded only as a regularization term, which does not sufficiently indicate that diversity should implicitly be treated as an...
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M. Joseph Hughes, S. Douglas Kaylor and Daniel J. Hayes
In the species-rich and structurally complex forests of the Eastern United States, disturbance events are often partial and therefore difficult to detect using remote sensing methods. Here we present a set of new algorithms, collectively called Vegetatio...
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Dimitris Fotakis, Loukas Kavouras and Lydia Zakynthinou
The Dynamic Facility Location problem is a generalization of the classic Facility Location problem, in which the distance metric between clients and facilities changes over time. Such metrics that develop as a function of time are usually called ?evolvin...
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Linjing Hu, Jiachen Wang, Zhaoze Guo and Tengda Zheng
Power load forecasting plays an important role in power systems, and the accuracy of load forecasting is of vital importance to power system planning as well as economic efficiency. Power load data are nonsmooth, nonlinear time-series and ?noisy? data. T...
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