Results 21 to 30 of about 33,535 (242)

Predictive machine learning-based error correction in GPS/IMU localization to improve navigation of autonomous vehicles [PDF]

MATEC Web of Conferences
Precise localization is crucial for the safety-critical factor and effective navigation of autonomous vehicles. This applied research examines machine learning models’ use to estimate, predict and correct errors in Global Positioning System (GPS ...
Onyema Uchenna Charles, Shafik Mahmoud
doaj   +1 more source

A Lightweight Procedural Layer for Hybrid Experimental–Computational Workflows in Materials Science

Advanced Engineering Materials, EarlyView.
We unveil a prototype hybrid‐workflow framework that fuses automatedcomputation with hands‐on experiments. Built atop pyiron, a lightweight, parameterized layer translates procedure descriptions into executable manual steps, syncing instrument settings, human interventions, and data capture in real‐time today.
Steffen Brinckmann, Sarath Menon, Raphael Röske, Johannes Steinhülb, Michael Selzer, Jan Janssen, Tilmann Hickel, Ruth Schwaiger, Jörg Neugebauer   +8 more
wiley   +1 more source

DigiChrom: A Domain Ontology for Semantic Representation of Trivalent Chromium Platings and Its Large Language Model‐Based Alignment With Multiple Mid‐Level Ontologies

Advanced Engineering Materials, EarlyView.
Digitalizing electroplating requires both domain knowledge and interoperability. This work introduces PlatOn, a domain ontology for trivalent chromium plating and coating characterization, and a hybrid pipeline that aligns it to a mid‐level reference ontology by combining eight similarity metrics with language model reasoning. Expert‐validated mappings
Janik Harter, Elena Garcia Trelles, Christoph Schweizer, Thomas Seifert, Christoph Baumer, Mahmoud Elkady, Katja Feige, Daniel Grimme, Edgar Hoffmann, Andreas Waibel, Anke Walter   +10 more
wiley   +1 more source

Comparative analysis of deep neural network architectures for renewable energy forecasting: enhancing accuracy with meteorological and time-based features

Discover Sustainability
This study evaluates and differentiates five advanced machine learning models—LSTM, GRU, CNN-LSTM, Random Forest, and SVR—aimed at precisely estimating solar and wind power generation to enhance renewable energy forecasting.
Sunawar Khan, Tehseen Mazhar, Muhammad Amir Khan, Tariq Shahzad, Wasim Ahmad, Afsha Bibi, Mamoon M. Saeed, Habib Hamam   +7 more
doaj   +1 more source

Noise‐Limited Bit Precision in Ferroelectric Synaptic Transistors for High‐Resolution Neuromorphic Computing

Advanced Functional Materials, EarlyView.
Low‐frequency noise spectroscopy defines the resolvable conductance states of synaptic FeFETs by coupling read‐current fluctuation with usable dynamic range. The resulting noise‐limited bit precision establishes a universal, device‐agnostic reliability metric beyond the memory window, enabling quantitative benchmarking and rational design of high ...
Jaehong Park, Yeonseop Shin, Changhyeon Han, Sangwoo Kim, Eunchan Park, Youngchan Cho, Jaehong Min, Hyunjun Ahn, Jaekyoung Park, Cheol‐Woong Yang, Jangsaeng Kim, Daewoong Kwon, Wonjun Shin   +12 more
wiley   +1 more source

Intelligent Orthopedics: Machine Learning in Diagnosis of Bone Disease, Implants, and Bone Health Monitoring

Advanced Healthcare Materials, EarlyView.
Efficient recovery from traumatic or degenerative diseases is a great challenge, even after all the advancements in bone and cartilage regeneration. Machine learning (ML) algorithms have presented opportunities to enhance these aspects by accurately analyzing imaging data.
Maryam Kamaei, Hesam Mohammadi, Shamim Golafshan, Mojtaba Alehosseini, Mahsa Mohammadzadeh, Atefeh Golshirazi, Nasim Golafshan, Tatiane Eufrásio‐da‐Silva, Alireza Dolatshahi‐Pirouz, Gorka Orive   +9 more
wiley   +1 more source

Analisis Pengaruh Fungsi Aktivasi, Learning Rate Dan Momentum Dalam Menentukan Mean Square Error (MSE) Pada Jaringan Saraf Restricted Boltzmann Machines (RBM)

JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING, 2019
<p>Restricted boltzmann machines (RBM) merupakan algoritma pembelajaran jaringan syaraf tanpa pengawaas (<em>unsupervised learning</em>) yang hanya terdiri dari dua lapisan yang <em>visible layer</em> dan <em>hidden layer</em>.
Susilawati Susilawati, Muhathir Muhathir
openaire   +2 more sources

Hydrophobic MFI‐Type Zeolites via Alkali‐Cation‐Induced Defect Healing: Implications for Adsorbent and Catalyst Design

Advanced Materials, EarlyView.
Sub‐stoichiometric amounts of Na+ or K+ enhance defect healing during Silicalite‐1 (MFI) and TS‐1 calcination by promoting Si–O–Si annealing and healing framework vacancies. The resulting defect‐free zeolites are more hydrophobic and show improved butanol/water separation and improved activity and selectivity in the epoxidation of 1‐hexene, offering a ...
Christos Kanteler, Pau Ferri, Francisco Javier Escobar‐Bedia, Álvaro Mayoral, Alejandro Vidal‐Moya, Richard Kendra, Chen Lei, Miguel Palomino, Fernando Rey, Lukáš Grajciar, Christopher James Heard, Pedro Serna, Mercedes Boronat, Cristina Martínez, Manuel Moliner   +14 more
wiley   +1 more source

Leveraging Mechanical Resonances for the Selection of Promising Materials in Complex Phase Spaces

Advanced Materials Technologies, EarlyView.
A central challenge in materials science is the design of new functional materials with enhanced performance for targeted applications. This paper demonstrates that “listening” to materials through measuring their mechanical resonances is a rich, and largely untapped, platform for the rapid identification of promising novel compounds and a powerful ...
Christopher A. Mizzi, Osman El‐Atwani, Tannor T. J. Munroe, Saryu Fensin, Boris Maiorov   +4 more
wiley   +1 more source

Directional Latent Hybridization: Beyond Random Noise in Physics‐Informed Generative Inverse Design of Nonlinear Metamaterials

Advanced Materials Technologies, EarlyView.
A physics‐informed generative framework introduces Directional Latent Hybridization (DLH) for the deterministic inverse design of nonlinear metamaterials. By hybridizing dominant traits from parent geometries in the latent space, DLH overcomes the instabilities of stochastic models to ensure high structural precision at high densities.
Semin Ahn, Jun Young Choi, Sung‐Hoon Ahn   +2 more
wiley   +1 more source