17 papers · ranked by Valyu relevance
Quoc Tuan Nguyen Diep, Hoang Nhut Huynh, Thanh Ven Huynh, Minh Quan Cao Dinh + 2 more
'Minh Quan Cao Dinh' 'Anh Tu Tran' 'Trung Nghia Tran'] Title: Abstract Electrical Impedance Tomography (EIT) is a non-invasive method for imaging conductivity distributions within a target area. The inverse problem associated with EIT is nonlinear and ill-posed, leading to low spatial resolution reconstructions.…
Luca Blum, Mohamed Elgendi, Carlo Menon
This paper studied the effects of applying the Box-Cox transformation for classification tasks. Different optimization strategies were evaluated, and the results were promising on four synthetic datasets and two real-world datasets. A consistent improvement in accuracy was demonstrated using a grid exploration with…
Harini Narayanan, Joshua A. Hinckley, Rachel Barry, Brendan Dang + 4 more
'Lenna A. Wolffe' 'Adel Atari' 'Yuen-Yi Tseng' 'J. Christopher Love'] Optimizing operational conditions for complex biological systems used in life sciences research and biotechnology is an arduous task. Here, we apply a Bayesian Optimization-based iterative framework for experimental design to accelerate cell culture…
Wenjie Tang, Li Cao, Yaodan Chen, Binhe Chen + 4 more
'Heming Jia' 'Laith Abualigah' 'Xuewen Xia'] In recent years, swarm intelligence optimization methods have been increasingly applied in many fields such as mechanical design, microgrid scheduling, drone technology, neural network training, and multi-objective optimization. In this paper, a multi-strategy particle swarm…
Louise Rixon Fuchs, Atsuto Maki, Andreas Gällström, Andrzej Stateczny
'Andrzej Stateczny'] Imaging and mapping sonars such as forward-looking sonars (FLS) and side-scan sonars (SSS) are sensors frequently used onboard autonomous underwater vehicles. To acquire information from around the vehicle, it is desirable for these sonar systems to insonify a large area; thus, the sonar transmit…
Jianghui Liu, Baozhu Li, Yangfan Zhou, Xuhui Zhao + 2 more
'Mingchuan Zhang'] Adaptive algorithms are widely used because of their fast convergence rate for training deep neural networks (DNNs). However, the training cost becomes prohibitively expensive due to the computation of the full gradient when training complicated DNN. To reduce the computational cost, we present a…
Lulu Zhang, Zhi-Qin John Xu, Yaoyu Zhang, Chi-Hua Chen
Motivated by the impressive success of deep learning in a wide range of scientific and industrial applications, we explore in this work the application of deep learning into a specific class of optimization problems lacking explicit formulas for both objective function and constraints. Such optimization problems exist…
Ying Chen, Yue Tang, Bin Jiang, Yinan Zhao + 5 more
'Xianghong Tang' 'Xinyu Zhou' 'Wing Shing Chan' 'Liheng Zhou'] To solve error propagation and exorbitant computational complexity of signal detection in wireless multiple-input multiple-output-orthogonal frequency division multiplexing (MIMO-OFDM) systems, a low-complex and efficient signal detection with iterative…
Tomasz Gajewski, Natalia Staszak, Tomasz Garbowski, Michele Bacciocchi
The production of thin-walled beams with various cross-sections is increasingly automated and digitized. This allows producing complicated cross-section shapes with a very high precision. Thus, a new opportunity has appeared to optimize these types of products. The optimized parameters are not only the lengths of the…
Sebastian Blume, Tim Benedens, Dieter Schramm, Soufiene Djahel
Software sensors are playing an increasingly important role in current vehicle development. Such soft sensors can be based on both physical modeling and data-based modeling. Data-driven modeling is based on building a model purely on captured data which means that no system knowledge is required for the application. At…
Florian Gisperg, Robert Klausser, Mohamed Elshazly, Julian Kopp + 2 more
'Eva Přáda Brichtová' 'Oliver Spadiut'] Title: ABSTRACT Bayesian optimization is a stochastic, global black-box optimization algorithm. By combining Machine Learning with decision-making, the algorithm can optimally utilize information gained during experimentation to plan further experiments-while balancing…
Fabian Fröhlich, Peter K. Sorger, Hugues Berry
Ordinary differential equation (ODE) models are widely used to study biochemical reactions in cellular networks since they effectively describe the temporal evolution of these networks using mass action kinetics. The parameters of these models are rarely known a priori and must instead be estimated by calibration using…
Gengsheng L. Zeng
A restricted Boltzmann machine is a fully connected shallow neural network. It can be used to solve many challenging optimization problems. The Boltzmann machines are usually considered probability models. Probability models normally use nondeterministic algorithms to solve their parameters. The Hopfield network which…
Yangyang Liu, Pengyang Zhang, Yu Ru, Delin Wu + 4 more
'Niuniu Yin' 'Fansheng Meng' 'Zhongcheng Liu'] The complex environments and weak infrastructure constructions of hilly mountainous areas complicate the effective path planning for plant protection operations. Therefore, with the aim of improving the current status of complicated tea plant protections in hills and…
Søren Bertelsen, Sigurd Carlsen, Søren Furbo, Morten Bormann Nielsen + 2 more
an Open-Source Python Package for Easy Optimization of Real-World Processes Using Bayesian Optimization: Showcase of Features and Example of Use Authors: ['Søren Bertelsen' 'Sigurd Carlsen' 'Søren Furbo' 'Morten Bormann Nielsen' 'Aksel Obdrup' 'Rolf Taaning'] ProcessOptimizer is a Python package designed to provide…
Jonas Bjermo
The design of an achievement test is crucial for many reasons. This article focuses on a population’s ability growth between school grades. We define design as the allocating of test items concerning the difficulties. The objective is to present an optimal test design method for estimating the mean and percentile…
Daniel Molina-Pérez, Edgar Alfredo Portilla-Flores, Efrén Mezura-Montes, Eduardo Vega-Alvarado + 2 more
'Efrén Mezura-Montes' 'Eduardo Vega-Alvarado' 'María Bárbara Calva-Yañez' 'Thomas Stützle'] Mixed integer nonlinear programming (MINLP) addresses optimization problems that involve continuous and discrete/integer decision variables, as well as nonlinear functions. These problems often exhibit multiple discontinuous…