Results 151 to 160 of about 1,338,397 (280)

Dental Microwear Texture Analysis on Radular Teeth

Advanced Materials Interfaces, EarlyView.
3D microwear textures reveal how gastropod radular teeth respond to controlled abrasive contact. Distinct surface signatures distinguish regular abrasion from fracture‐dominated material removal, showing how mineralized coatings, fibrous cores, and substrate properties jointly shape wear and failure in a biological scraping interface.
Wencke Krings, Stanislav N. Gorb, Ellen Schulz‐Kornas   +2 more
wiley   +1 more source

A Novel Framework for Abnormal Risk Classification over Fetal Nuchal Translucency Using Adaptive Stochastic Gradient Descent Algorithm. [PDF]

Diagnostics (Basel), 2022
Verma D, Agrawal S, Iwendi C, Sharma B, Bhatia S, Basheer S.   +5 more
europepmc   +1 more source

Recent Advances of Slip Sensors for Smart Robotics

Advanced Materials Technologies, EarlyView.
This review summarizes recent progress in robotic slip sensors across mechanical, electrical, thermal, optical, magnetic, and acoustic mechanisms, offering a comprehensive reference for the selection of slip sensors in robotic applications. In addition, current challenges and emerging trends are identified to advance the development of robust, adaptive,
Xingyu Zhang, Liao Wu, Shuai He, Zhao Sha, Yuwan Huang, Shuying Wu, Dewei Chu, Chun H. Wang, Shuhua Peng   +8 more
wiley   +1 more source

Non-Iterative Phase-Only Hologram Generation via Stochastic Gradient Descent Optimization

Photonics
In this work, we explored, for the first time, to the best of our knowledge, the potential of stochastic gradient descent (SGD) to optimize random phase functions for application in non-iterative phase-only hologram generation.
Alejandro Velez-Zea, John Fredy Barrera-Ramírez   +1 more
doaj   +1 more source

An Improvised Sentiment Analysis Model on Twitter Data Using Stochastic Gradient Descent (SGD) Optimization Algorithm in Stochastic Gate Neural Network (SGNN). [PDF]

SN Comput Sci, 2023
Vidyashree KP, Rajendra AB.
europepmc   +1 more source

Advanced Manufacturing of Composite‐Based Systems for Energy Applications

Advanced Materials Technologies, EarlyView.
Advanced manufacturing enables the integration of polymers, ceramics, metal oxides, and composites into architected microstructures with tailored transport pathways. By coupling material selection, manufacturing strategy, and structural design, multifunctional energy systems can simultaneously improve electrochemical performance, thermal management ...
Sri Vaishnavi Thummalapalli, Dhanush Patil, Libin Yang, M. Taylor Sobczak, Anshul Kulkarni, Xiao Sun, Arunachalam Ramanathan, Tiantian Li, Ramaraja P. Ramasamy, Jason J. Locklin, Yanyu Chen, Xianqiao Wang, Arunachala Mada Kannan, Kenan Song   +13 more
wiley   +1 more source

A Novel Sophia-SPGD (Stochastic Parallel Gradient Descent) Optimization Method for Wavefront Correction in WFS-Less AO (Wavefront Sensorless Adaptive Optics) Systems

Photonics
In wavefront sensorless adaptive optics (WFS-less AO) systems, stochastic parallel gradient descent (SPGD) is the primary optimization method for correcting wavefront distortions. However, as the intensity of atmospheric turbulence interference increases,
Peng Chen, Wenjie Yang, Yongqi Ge, Zhiguang Zhang, Xianshuo Li, Zhengqing Qi, Huizhen Yang   +6 more
doaj   +1 more source

Using the Stochastic Gradient Descent Optimization Algorithm on Estimating of Reactivity Ratios. [PDF]

Materials (Basel), 2021
Fazakas-Anca IS, Modrea A, Vlase S.
europepmc   +1 more source

On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems [PDF]

This paper analyzes the trajectories of stochastic gradient descent (SGD) to help understand the algorithm’s convergence properties in non-convex problems.
Mertikopoulos, Panayotis, Kavis, Ali, Cevher, Volkan, Hallak, Nadav   +3 more
core  

Certain Systems Arising In Stochastic Gradient Descent [PDF]

, 2019
Stochastic approximations is a rich branch of probability theory and has a wide range of application. Here we study stochastic approximations from the perspective of gradient descent.
Karatapanis, Konstantinos
core