Results 91 to 100 of about 97,762 (292)

Quasi-Newton Methods for tSNE

, 2020
TSNE is a popular technique for visualizing high-dimensional data. It finds a low-dimensional representation of the data, also known as embedding, by optimizing a highly non-linear cost function.
Chaves De Plaza, Nicolas (author)
core  

Machine learning‐driven advances in carbon‐based quantum dots: Opportunities accompanied by challenges

Responsive Materials, EarlyView.
Machine learning provides a unifying framework to connect structure, fluorescence properties, and applications of carbon‐based quantum dots. This review highlights how data‐driven strategies enable fluorescence regulation, reveal underlying mechanisms, and accelerate the rational design of functional carbon dots.
Liangfeng Chen, Siwei Yang, Xiangqian Cui, Ziheng Liu, Hang Wang, Yongqiang Li, Peng He, Miao Wang, Guqiao Ding   +8 more
wiley   +1 more source

Key Technical Fields and Future Outlooks of Space Manipulators: A Survey

SmartBot, EarlyView.
This paper systematically reviews the technological development of space manipulators, emphasizing the unique challenges posed by space environments. It examines four areas: structural design, modeling, planning, and control, while introducing typical ground test platforms.
Gang Chen, Zeyuan Huang, Ying Yan, Xiaokang An, Zhikai Zhao, Jirong Lin, Xiangrong Zhao, Xianyuan Gao, Junyi Zong, Xuewen Jia, Zhipeng Yao, Jianliang Liao, Ning Ji   +12 more
wiley   +1 more source

Attitude Estimation Fusing Quasi-Newton and Cubature Kalman Filtering for Inertial Navigation System Aided With Magnetic Sensors

IEEE Access, 2018
In the complex underwater environment, the performance of microelectro-mechanical system sensors is degraded sharply and the errors will become much larger.
Haoqian Huang, Jun Zhou, Jun Zhang, Yuan Yang, Rui Song, Jianfeng Chen, Jiajin Zhang   +6 more
doaj   +1 more source

Practical quasi-Newton methods for solving nonlinear systems

, 2000
Practical quasi-Newton methods for solving nonlinear systems are surveyed. The definition of quasi-Newton methods that includes Newton's method as a particular case is adopted.
Martı́nez, José Mario, José Mario Martı́nez   +1 more
core   +1 more source

Machine Learning‐Driven Capillary Microfluidic Design Automation for Programmable Gradient Generation and Antimicrobial Testing

Small, EarlyView.
TCG‐CMDA, a machine learning‐guided capillary microfluidic design automation platform, enables automated design of tree‐shaped concentration gradient generators for programmable mixing of two agents. The pump‐free chip supports synchronized passive flow and programmable gradient formation, providing a practical framework for decentralized point‐of‐care
Mahmood Khalghollah, Azam Zare, Sorosh Abdollahi, Behrouz H. Far, Amir Sanati Nezhad   +4 more
wiley   +1 more source

A Novel Approach to Energy Management in Electric Steelworks

steel research international, EarlyView.
Feed‐forward neural networks are exploited to estimate electric energy consumptions of the electric arc furnace and ladle furnace processes. The models are used to optimize production schedule so that more energy intensive grades are produced when the cost of energy is lower.
Valentina Colla, Stefano Dettori, Silvia Cateni, Marco Vannucci, Antonella Vignali, Claudio Mocci, Irene Dovichi, Davide Chionna, Lorenzo Vannini, Enrico Paluzzano, Costanzo Pietrosanti, Davide Onesti, Daniele Venier   +12 more
wiley   +1 more source

ANÁLISIS DE LA FRONTERA DE CONVERGENCIA EN MÉTODOS CUASI-NEWTON CON MATRICES DISPERSAS Y SU APLICACIÓN A LA GEOMECÁNICA DE VOLADURAS

Avances en Ciencias e Ingeniería
We delimit the convergence frontier of sparse–matrix quasi-Newton algorithms for rock-blasting simulations. Lip- schitz/Hölder bounds yield Kantorovich radii that mark when the secant matrix preserves contraction.
Fabian León, Luis Rojas, Cristian Gonzalez, Beatriz Hernández, José García   +4 more
doaj   +1 more source

LEARNING ALGORITHM EFFECT ON MULTILAYER FEED FORWARD ARTIFICIAL NEURAL NETWORK PERFORMANCE IN IMAGE CODING [PDF]

Journal of Engineering Science and Technology, 2007
One of the essential factors that affect the performance of Artificial Neural Networks is the learning algorithm. The performance of Multilayer Feed Forward Artificial Neural Network performance in image compression using different learning algorithms is
OMER MAHMOUD, FARHAT ANWAR, MOMOH JIMOH E. SALAMI   +2 more
doaj  

Damped Inexact Quasi-Newton Methods

, 1981
The inexact quasi-Newton methods are very attractive methods for large scale optimization since they require only an approximate solution of the linear system of equations for each iteration.
Steihaug, Trond
core