Results 71 to 80 of about 105,450 (329)

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

PointGeo: Geometry Transformer for Point Cloud Analysis

CAAI Transactions on Intelligence Technology
Point cloud processing plays a crucial role in tasks such as point cloud classification, partial segmentation and semantic segmentation. However, existing processing frameworks are constrained by several challenges, such as recognising features in ...
Li An, Pengbo Zhou, Mingquan Zhou, Yong Wang, Guohua Geng, Yangyang Liu   +5 more
doaj   +1 more source

A Neural Network Based System for Efficient Semantic Segmentation of Radar Point Clouds [PDF]

, 2021
Alessandro Cennamo, Florian Kaestner, Anton Kummert   +2 more
openalex   +1 more source

Hydrogen‐Bond‐Rich Supramolecular Multiblock Copolymers Facilitate Rapid Zn2+ Migration in Quasi‐Solid‐State Zinc‐Ion Batteries

Advanced Functional Materials, EarlyView.
The disordered growth of dendrites, corrosion, parasitic side reactions, slow de‐solvation kinetics, and inherent safety risks significantly hinder the practical deployment of conventional liquid electrolyte zinc‐ion batteries. In contrast, the novel PU‐EG+DMPA‐Zn polyurethane quasi‐solid‐state electrolyte, enriched with abundant polar functional ...
Ruiqi Liu, Changding Wang, Fu Wan, Kaida Hu, Da Yang, Yaoyang Xia, Wenwei Yin, Yupeng Lin, Yuejiao Chen, Shuhui Sun, Weigen Chen   +10 more
wiley   +1 more source

Spectrally Tunable 2D Material‐Based Infrared Photodetectors for Intelligent Optoelectronics

Advanced Functional Materials, EarlyView.
Intelligent optoelectronics through spectral engineering of 2D material‐based infrared photodetectors. Abstract The evolution of intelligent optoelectronic systems is driven by artificial intelligence (AI). However, their practical realization hinges on the ability to dynamically capture and process optical signals across a broad infrared (IR) spectrum.
Junheon Ha, Yingshan Ma, Yong Nam An, Sung‐Un An, Hyeon Hak Jung, Suvi‐Tuuli Varjamo, Jiwon Yoo, Junho Min, Hanvit Kim, Faisal Ahmed, Sang Hoon Chae, Young Min Song, Weiwei Cai, Tawfique Hasan, Zhipei Sun, Dong‐Ho Kang, Hyeon‐Jin Shin, Yunyun Dai, Hoon Hahn Yoon   +18 more
wiley   +1 more source

Point Cloud Segmentation Algorithm Based on Improved Euclidean Clustering

IEEE Access
Point cloud segmentation is a crucial technique for object recognition and localization, widely employed in various applications such as point cloud registration, 3D reconstruction, object recognition, and robotic grasping.
Fangrui Chen, Feifei Xie, Lin Sun, Yuchao Gu, Zhipeng Zhang, Jinpeng Chen, Jinrui Zhang, Mingzhe Yi   +7 more
doaj   +1 more source

Feature Surface Segmentation and Recognition Method for Point Cloud Model [PDF]

Jisuanji gongcheng, 2018
Aiming at the problem of slow speed and poor accuracy of model segmentation and feature surface recognition,a point cloud segmentation and surface recognition method based on connected component labeling and probabilistic method is proposed.A zero-value ...
YUAN Xiaocui,CHEN Huawei
doaj   +1 more source

Point Cloud Semantic Segmentation using Multi Scale Sparse Convolution Neural Network [PDF]

, 2022
Yunzheng Su, Jiang, Lei, Cao, Jie
openalex   +1 more source

Dynamic Convolution for 3D Point Cloud Instance Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022
We propose an approach to instance segmentation from 3D point clouds based on dynamic convolution. This enables it to adapt, at inference, to varying feature and object scales. Doing so avoids some pitfalls of bottom up approaches, including a dependence on hyper-parameter tuning and heuristic post-processing pipelines to compensate for the inevitable ...
Tong He, Chunhua Shen, Anton van den Hengel   +2 more
openaire   +4 more sources

Digital Discovery of Synthesizable Metal−Organic Frameworks via Molecular Dynamics‑Informed, High‑Fidelity Deep Learning

Advanced Functional Materials, EarlyView.
Tabular foundation model interrogates the synthetic likelihood of metal−organic frameworks. Abstract Metal–organic frameworks (MOFs) are celebrated for their chemical and structural versatility, and in‑silico screening has significantly accelerated their discovery; yet most hypothetical MOFs (hMOFs) never reach the bench because their synthetic ...
Xiaoyu Wu, Rui Zheng, Quan Liu, Jianwen Jiang   +3 more
wiley   +1 more source