Results 91 to 100 of about 5,787,304 (258)

Artificial Intelligence‐Driven Inverse Design of Singlet Fission Candidates in the Acene family

Advanced Science, EarlyView.
In this work, we describe an AI‐driven inverse design platform predicting S1 and T1 energies across acenes with high accuracy. Using Hammett σ constants as descriptors and RNN models coupled with optimization algorithms, it efficiently explores ≈1014 structures, uncovering novel SF candidates from benzene to pentacene. Freely accessible at https://alba.
Rafael G. Uceda, Boris Pérez‐Cañedo, Sandra Míguez‐Lago, Carlos M. Cruz, Joaquín J. Torres, Omar Núñez, Luis Álvarez de Cienfuegos, Antonio J. Mota, Delia Miguel, Juan M. Cuerva   +9 more
wiley   +1 more source

Transforming oil market analysis: A novel GAN + LSTM predictive framework

Next Energy
A novel method of predicting the crude oil WTI futures prices based on a data set covering April 12, 2009 through January 7, 2024. To capture complex market dynamics more precisely, it incorporates key market factors such as open, high, and low price ...
Prity Kumari, G.Y. Chandan, Satish Kumar M   +2 more
doaj   +1 more source

Physics‐Aware Machine‐Learning‐Driven Inverse Design of Broadband Ultra‐Open Acoustic Metamaterials

Advanced Science, EarlyView.
A physics‐aware machine‐learning framework enables inverse design of ultra‐open acoustic silencers by decoupling spectral and radial design spaces. The approach rapidly identifies broadband, compact, and highly ventilated architectures, while revealing hidden linear design rules that link geometry, impedance matching, and acoustic performance.
Zhiwei Yang, Mengyu Li, Xiaohang Xie, Ao Chen, Thomas G. Bifano, Xin Zhang   +5 more
wiley   +1 more source

CoSP: Reconfigurable Metamaterial Inverse Design via Contrastive Pretrained Large Language Model

Advanced Science, EarlyView.
In this work, CoSP (contrastive multi‐state pretrain), an intelligent inverse design method for reconfigurable metamaterials based on a contrastive pretrained large language model, is proposed. Numerical experiments demonstrate that CoSP can design reconfigurable metamaterial structures for multi‐state, multi‐band optical responses, showing great ...
Shujie Yang, Yuqi Zhang, Xuzhe Zhao, Yansong Tang, Kaichen Dong   +4 more
wiley   +1 more source

EndoTac: An Endoscopic Camera‐Based Tactile Sensor with High Sensitivity for Minimally Invasive Surgery

Advanced Science, EarlyView.
EndoTac presents a trocar‐compatible endoscopic vision‐based tactile sensor that uses a convex mirror to enlarge side‐facing tactile coverage during minimally invasive vessel palpation. Distorted tactile images are unwarped and processed by a learning model to estimate vascular deformation, enabling sensitive, spatially distributed tactile perception ...
Yupeng Wang, Nicolas Raison, Xuyang Zhang, Aijia Yang, Lukas Lindenroth, Shan Luo   +5 more
wiley   +1 more source

Impact of signaling schemes on iterative linear minimum-mean-square-error detection

, 2008
In this paper, we study the iterative detection problem for a coded system with multi-ary modulation. We show that, with iterative linear minimum-mean-square-error (LMMSE) detection, superposition coded modulation (SCM) can provide performance superior ...
Jun Tong (19588204), Li Ping (427468), Xiaojun Yuan (410299), Qinghua Guo (19588207)   +3 more
core   +1 more source

Comparison of AI Models for Predicting Shelf Life of Roasted Coffee Drink

Transactions on Informatics and Data Science
This paper explores the application of artificial intelligence (AI), specifically artificial neural networks (ANN), to predict the shelf life of roasted coffee beverages. The research focuses on two advanced AI models: a competitive neural network based
Sumit Goyal
doaj   +1 more source

Data‐Driven Design of Self‐Adhesive Epidermal Electrodes and Sensors

Advanced Science, EarlyView.
This work presents self‐adhesive, stretchable epidermal electrodes and sensors developed through a data‐driven design framework that integrates artificial neural networks with genetic algorithms. By tailoring optimization objectives, either maximizing electrical conductivity and adhesion or enhancing piezoresistive sensitivity, the study enables the ...
Xuan Li, Shilei Wang, Milad Razbin, Danish Tahir, Chen Sang, Shuhua Peng, Markus Müllner, Wenlong Cheng, Wei Chen, Chun Hui Wang, Shuying Wu   +10 more
wiley   +1 more source

Comparing Huber's M-Estimator Function with the

, 2000
In any data set there some of the data will be bad or noisy. This study identifies two types of noise and investigates the effect of each in the training data of backpropagation neural networks.
David Clark, Mean Square Error
core  

Drop‐Location‐Insensitive Droplet‐Based Electricity Generator Using Polydimethylsiloxane Slippery Covalently Attached Liquid Coatings

Advanced Science, EarlyView.
Drop‐location sensitivity remains a key barrier to practical droplet‐based electricity generators (DEGs). Here, a polydimethylsiloxane slippery covalently attached liquid (PDMS‐SCAL)‐coated interface promotes droplet sliding instead of splitting or bouncing, allowing stable electricity generation from randomly falling raindrops. The resulting PDMS‐SCAL‐
Jung Bin Yang, Donghyeon Yoo, Hideaki Teshima, Donghan Lee, Dongwhi Choi, Dong Rip Kim, Nenad Miljkovic   +6 more
wiley   +1 more source