Results 271 to 280 of about 1,011,171 (338)

Topology and Material Optimization in Ultra‐Soft Magneto‐Active Structures: Making Advantage of Residual Anisotropies

Advanced Materials, EarlyView.
Residual magnetization induces pronounced mechanical anisotropy in ultra‐soft magnetorheological elastomers, shaping deformation and actuation even without external magnetic fields. This study introduces a computational‐experimental framework integrating magneto‐mechanical coupling into topology optimization for designing soft magnetic actuators with ...
Carlos Perez‐Garcia, Rogelio Ortigosa, Jesús Martínez‐Frutos, Daniel Garcia‐Gonzalez   +3 more
wiley   +1 more source

Assessment of the Aging State for Transformer Oil-Barrier Insulation by Raman Spectroscopy and Optimized Support Vector Machine. [PDF]

Sensors (Basel)
Liu D, Lu B, Wu W, Zhou W, Liu W, Sun Y, Wu S, Shi G, Yuan L.   +8 more
europepmc   +1 more source

SISTEM DETEKSI OTOMATIS JAMUR KULIT PADA PUNGGUNG MANUSIA MENGGUNAKAN SUPPORT VECTOR MACHINE

Helena dorothea Mbura, Yampi R Kaesmetan
openalex   +2 more sources

“Tear‐And‐Stack” Twisted SrTiO3 Moiré Superlattices for Precise Interfacial Reconstruction and Polar Topology

Advanced Materials, EarlyView.
The tear‐and‐stack method enables the creation of twisted SrTiO3 bilayers with accurate twist‐angle control, which yield atomically sharp oxide moiré superlattices with emergent exotic topological polar vortices, thereby opening a new pathway for twistronics based on 2D‐like non‐van der Waals oxides.
Yingli Zhang, Jinxin Ge, Shengyao Su, Yuhao Li, Wenxi Zhang, Longji Lyu, Jiahao Song, Yuxin Liu, Yihan Lei, Haopeng Du, Gaokuo Zhong, Boyuan Huang, Jiangyu Li, Changjian Li   +13 more
wiley   +1 more source

Forecasting for Haditha reservoir inflow in the West of Iraq using Support Vector Machine (SVM). [PDF]

PLoS One
Mahmood OA, Sulaiman SO, Al-Jumeily D.
europepmc   +1 more source

APPLICATION OF THE SUPPORT VECTOR MACHINE, LIGHT GRADIENT BOOSTING MACHINE, ADAPTIVE BOOSTING, AND HYBRID ADABOOST-SVM MODEL ON CUSTOMERS CHURN DATA

Felice Elena, Ridwan Dwi Irawan, Benny Yong   +2 more
openalex   +2 more sources

Property Optimization of Mn–Ni–Al Oxide Thermistors via an Integrated Framework of Efficient Composition Exploration and Image Analysis

Advanced Materials Interfaces, EarlyView.
In Mn–Ni–Al oxide thermistor materials, we combine efficient composition‐space sampling with convolutional neural networks trained on multimodal Scanning Electron Microscopy – Energy Dispersive X‐ray Spectroscopy (SEM‐EDS) images to construct models predicting resistance and B constant, revealing spinel‐like microstructural regions that control ...
Yo Kato, Keisuke Makino, Shogo Hashimura, Nozomi Arakawa, Hayami Takeda, Naoto Tanibata, Masanobu Nakayama, Naohiro Niizeki, Takayuki Nakaya   +8 more
wiley   +1 more source

Predicting the bacterial host range of plasmid genomes using the language model-based one-class support vector machine algorithm. [PDF]

Microb Genom
Feng T, Chen X, Wu S, Tang W, Zhou H, Fang Z.   +5 more
europepmc   +1 more source

A hybrid unsupervised machine learning model with spectral clustering and semi-supervised support vector machine for credit risk assessment. [PDF]

PLoS One
Yu T, Huang W, Tang X, Zheng D.
europepmc   +1 more source