Results 261 to 270 of about 1,701,375 (322)

Roadmap on Artificial Intelligence‐Augmented Additive Manufacturing

Advanced Intelligent Systems, EarlyView.
This Roadmap outlines the transformative role of artificial intelligence‐augmented additive manufacturing, highlighting advances in design, monitoring, and product development. By integrating tools such as generative design, computer vision, digital twins, and closed‐loop control, it presents pathways toward smart, scalable, and autonomous additive ...
Ali Zolfagharian, Liuchao Jin, Qi Ge, Wei‐Hsin Liao, Andrés Díaz Lantada, Francisco Franco Martínez, Tianyu Zhang, Tao Liu, Charlie C. L. Wang, Mohammad Hossein Mosallanejad, Reza Ghanavati, Abdollah Saboori, Alejandro De Blas De Miguel, William Solórzano‐Requejo, Yi Cai, Xiangyang Dong, Huangyi Qu, Najmeh Samadiani, Guangyan Huang, Austin Downey, Yanzhou Fu, Lang Yuan, Tsz‐Kwan (Glory) Lee, Arbind Agrahari Baniya, Eisha Waseem, Abdul Rahman Sani, Abbas Z. Kouzani, Yijia Wu, Markus P. Nemitz, Masoud Shirzad, Dageon Oh, Seung Yun Nam, Amedeo Franco Bonatti, Irene Chiesa, Gabriele Maria Fortunato, Giovanni Vozzi, Carmelo De Maria, Mahdi Bodaghi   +37 more
wiley   +1 more source

Multitarget Recognition of Flower Images Based on Lightweight Deep Neural Network and Transfer Learning

Advanced Intelligent Systems, EarlyView.
This article proposes a lightweight YOLOv4‐based detection model using MobileNetV3 or CSPDarknet53_tiny, achieving 30+ FPS and higher mAP. It also presents a ShuffleNet‐based classification model with transfer learning and GAN‐augmented images, improving generalization and accuracy.
Qingyang Liu, Yanrong Hu, Hongjiu Liu
wiley   +1 more source

gnSPADE: Incorporating Gene Network Structures Enhances Reference‐Free Deconvolution in Spatial Transcriptomics

Advanced Intelligent Systems, EarlyView.
gnSPADE integrates gene‐network structures into a probabilistic topic modeling framework to achieve reference‐free cell‐type deconvolution in spatial transcriptomics. By embedding gene connectivity within the generative process, gnSPADE enhances biological interpretability and accuracy across simulated and real datasets, revealing spatial organization ...
Aoqi Xie, Yuehua Cui
wiley   +1 more source

Optisense: Computational Optimization for Strain Sensor Placement in Wearable Motion Tracking Systems

Advanced Intelligent Systems, EarlyView.
A computational framework for optimizing strain sensor placement in wearable motion tracking systems is presented. By combining dense strain mapping with a genetic algorithm, the method discovers counterintuitive yet highly effective configurations that reduce joint angle error by 32%.
Minu Kim, Juhyung Ahn, Jieun Seo, Steve Park, Joo Yong Sim   +4 more
wiley   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure Smart Grids

IEEE Transactions on Industrial Informatics, 2018
Electricity theft is harmful to power grids. Integrating information flows with energy flows, smart grids can help to solve the problem of electricity theft owning to the availability of massive data generated from smart grids.
Yang Yatao, Xiangdong, Hong-Ning Dai
exaly   +2 more sources

Smart Grid

International Journal of E-Politics, 2013
With no less than half a billion people in the world without electricity supply, and electricity being the back bone for technological development, it makes sense that electricity is the center of discussion. While innovation and technology have radically transformed other industrial sectors, the electric system, has continued to operate the same way ...
Nikhil Swaroop Kaluvala, Abbe Forman
  +4 more sources

Demand response and smart grids—A survey

Renewable and Sustainable Energy Reviews, 2014
Pierluigi Siano
exaly   +2 more sources

Smart and Practical Privacy-Preserving Data Aggregation for Fog-Based Smart Grids

IEEE Transactions on Information Forensics and Security, 2021
With the increasingly powerful and extensive deployment of edge devices, edge/fog computing enables customers to manage and analyze data locally, and extends computing power and data analysis applications to network edges.
Shuai Zhao, Fenghua Li, Hongwei Li, Rongxing Lu, Siqi Ren, Haiyong Bao, Jianhong Lin, Song Han   +7 more
semanticscholar   +1 more source

???????????????????? ???????????? ?????????????? ?? ???????????????????????? Smart Grid ?????????? ???? ???????? ???????????????? ???? ???????????????????? ?? ???????????????????? ???????????????? ????????????????????????????????

2019
???????????????? ?????????????????????????? ?????????????????????? ?????????????????????? ?????????????????????????? ???????????????? ???? ???????????????????? ?? ???????????????????? ???????????????? ???????????????????????????????? ??? ???????????????? ???????????? ?????????????? ?? ?????????????? ????????.
null Maheswari M., null Gunasekharan S.
  +9 more sources

Smart Grid, Safe Grid

IEEE Power and Energy Magazine, 2012
The existing power delivery system is vulnerable to both natural disasters and intentional attack. A successful terrorist attempt to disrupt the power delivery system could have adverse effects on national security, the economy, and the lives of every citizen.
S. Massoud Amin, Anthony M. Giacomoni
openaire   +1 more source