Results 91 to 100 of about 5,262,248 (269)

Interpretable Machine Learning for Bandgap Prediction and Descriptor‐Guided Design Rules of Phosphates

Advanced Intelligent Discovery, EarlyView.
An explainable CatBoost model was trained to predict the bandgaps of 474 phosphate crystals based on composition and density descriptors. SHAP analysis identified two key variables—d‐electron‐count dispersion and atomic‐density dispersion—as the primary drivers of the model's predictions.
Wenhu Wang, Abudukadi Tudi, Ran An, Zhihua Yang   +3 more
wiley   +1 more source

A simplified space vector modulation for the single-phase active power decoupling quasi-Z-source inverter

International Journal of Electrical Power & Energy Systems
The double-line frequency ripple power of the single-phase quasi-Z source inverter (qZSI) will result in a large designed qZS impedance on the dc side, which can be greatly reduced by the coupled-type active power decoupling (APD) method.
Rui Zhang, Shunliang Wang, Yuan Li, Junpeng Ma, Peng Wang, Tianqi Liu   +5 more
doaj   +1 more source

Large‐Scale Machine Learning to Screen for Small‐Molecule Senolytics

Advanced Intelligent Discovery, EarlyView.
A consistent workflow underpins all experiments in this study. A dedicated model‐selection dataset first identifies optimal hyperparameters for each algorithm. Models are then trained and rigorously evaluated on independent sets of molecules using the senolytic ratio SR. Comprehensive hyperparameter exploration across SMILES representations, task types,
Alexis Dougha, Jesus Gil, Pedro J. Ballester   +2 more
wiley   +1 more source

Synthetic space vector modulation [PDF]

, 2013
Alternating current motors are used throughout the fleet because of their rugged construction and nearly maintenance free operation. Since the U.S. Navy is exploring and acting on the possibilities of DC distribution systems, the need exists for simple ...
Rogers, L Warren
core   +2 more sources

Unsupervised two-class & multi-class support vector machines for abnormal traffic characterization. [PDF]

, 2009
Although measurement-based real-time traffic classification has received considerable research attention, the timing constraints imposed by the high accuracy requirements and the learning phase of the algorithms employed still remain a challenge. In this
Kim, Hyun-chul, Hutchison, D., Pezaros, D., Pezaros, Dimitrios P., Marnerides, A., Kim, H., Marnerides, Angelos, Hutchison, David   +7 more
core  

Toward Complex In‐Car Environment Human–Vehicle Interactions Through Smart Glasses and sEMG‐Based Gesture Recognition

Advanced Intelligent Systems, EarlyView.
This study proposes a novel weighted random forest multimodal fusion method that combines smart glasses and sEMG data for in‐vehicle gesture interaction. It realizes stable performance in dim, occluded, and other constrained scenarios, providing feasible solutions and laying a foundation for universal human–machine interaction.
Wenbo Zhang, Wenting Wei, Kuang Li, Chuyuan Bian, Junkai Xu, Jianing Qiu, Yuxi Lu, Bin He, Shuo Jiang   +8 more
wiley   +1 more source

Privacy-Preserving Classification of Horizontally Partitioned Data via Random Kernels [PDF]

, 2007
We propose a novel privacy-preserving nonlinear support vector machine (SVM) classifier for a data matrix A whose columns represent input space features and whose individual rows are divided into groups of rows.
Mangasarian, Olvi, Wild, E
core  

A Soft Robotic Finger With Deep Learning‐Enhanced Tactile Sensing for Texture and Softness Recognition

Advanced Intelligent Systems, EarlyView.
Inspired by human touch, a tendon‐driven soft robotic finger combines multimodal tactile sensing and deep learning to simultaneously perceive texture and softness on deformable surfaces. A CNN‐LSTM model fuses pressure, accelerometer, and gyroscope signals to accurately classify material properties, achieving up to 95.4% texture and 97.0% softness ...
Gorkem Anil Al, Arya Shabani, Nicos Hadjigeorgiou, James Fant‐Male   +3 more
wiley   +1 more source

Benchmarking least squares support vector machine classifiers. [PDF]

In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a ( convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LS-SVMs), a least squares cost function
Suykens, Johan, Vandewalle, Joos, De Moor, Bart, Van Gestel, Tony, Vanthienen, Jan, Baesens, Bart, Dedene, Guido, Viaene, Stijn   +7 more
core  

Accelerating Materials Discovery: A Review of Machine Learning in X‐Ray Absorption Spectroscopy

Advanced Intelligent Systems, EarlyView.
This review systematically details how machine learning transforms X‐ray absorption spectroscopy (XAS) analysis. It covers advanced deep learning architectures for structure‐spectra mapping and inverse tasks, while discussing key challenges like the simulation‐to‐reality gap.
Melaku Lake Tegegne, Haodong Yao, Liyuan Wu, Yihe Pang, Fazhi Qi, Lina Zhao   +5 more
wiley   +1 more source