Results 21 to 30 of about 13,553,800 (229)

Incremental Sparse Density-Weighted Twin Support Vector Regression [PDF]

Jisuanji gongcheng
The Density-Weighted Twin Support Vector Regression(DWTSVR) is a regression algorithm that reflects the internal distribution of data with high prediction accuracy and robustness.
Weijie DING, Binjie GU, Feng PAN
doaj   +1 more source

LFM: An R package for laplace factor model

SoftwareX
The Laplace Factor Model (LFM) is a valuable mathematical tool used in statistics, machine learning, and data analysis. It uses the Laplace distribution to capture data sparsity and uncertainty, effectively handling complex, large-scale data.
Siqi Liu, Guangbao Guo
doaj   +1 more source

TFM: An R package for truncated factor model

SoftwareX
The Truncated Factor Model (TFM) is a statistical model for analyzing high-dimensional truncated data, leveraging sparsity and online learning to extract common factors.
Beibei Wu, Guangbao Guo
doaj   +1 more source

IoT and Engagement in the Ubiquitous Museum

Sensors, 2019
In increasingly hyper-connected societies, where individuals rely on short and fast online communications to consume information, museums face a significant survival challenge.
Roberto Pierdicca, Manuel Marques-Pita, Marina Paolanti, Eva Savina Malinverni   +3 more
doaj   +1 more source

Field Report from Collaborative Research Center 1625: Heterogeneous Research Data Management Using Ontology Representations

Advanced Engineering Materials, EarlyView.
A unified research data management framework for heterogeneous materials data is presented. The system integrates multimodal datasets using ontologies and knowledge graphs, enabling interoperability and FAIR (findable, accessible, interoperable, reusable) data principles. By linking data across scales and workflows, it supports reproducible, Artifitial
Doaa Mohamed, Samuel García Vázquez, Behnam Mardani, Victor Dudarev, Alfred Ludwig, Maribel Acosta, Markus Stricker   +6 more
wiley   +1 more source

A Knowledge‐Based Approach for Understanding and Managing Additive Manufacturing Data

Advanced Engineering Materials, EarlyView.
Additive manufacturing processes generate a large amount of data. Effectively managing, understanding, and retrieving information from this data remains a major challenge. Therefore, we propose an ontology‐based approach to integrate heterogeneous data, enable semantic queries, and support decision‐making.
Mina Abd Nikooie Pour, Prithwish Tarafder, Anton Wiberg, Huanyu Li, Johan Moverare, Patrick Lambrix   +5 more
wiley   +1 more source

A Practical Noise2Noise Denoising Pipeline for High‐Throughput Raman Spectroscopy

Advanced Engineering Materials, EarlyView.
A lightweight and reproducible denoising pipeline for high‐throughput Raman spectroscopy is introduced, based on a 1D convolutional autoencoder trained with a Noise2Noise strategy. Using only repeated short‐exposure acquisitions, the method suppresses stochastic noise without reference spectra, enabling reliable spectral reconstruction while preserving
David Martin‐Calle, Cesar Alvarez‐Llamas, Vincent Motto‐Ros, Christophe Dujardin, Jérémie Margueritat, David Rodney   +5 more
wiley   +1 more source

Bayesian modelling of music : algorithmic advances and experimental studies of shift-invariant sparse coding [PDF]

, 2005
PhDIn order to perform many signal processing tasks such as classification, pattern recognition and coding, it is helpful to specify a signal model in terms of meaningful signal structures.
Blumensath, Thomas
core   +3 more sources

Online Monitoring and Fault Diagnosis for High-Dimensional Stream with Application in Electron Probe X-Ray Microanalysis

Entropy
This study introduces an innovative two-stage framework for monitoring and diagnosing high-dimensional data streams with sparse changes. The first stage utilizes an exponentially weighted moving average (EWMA) statistic for online monitoring, identifying
Tao Wang, Yunfei Guo, Fubo Zhu, Zhonghua Li   +3 more
doaj   +1 more source

Toward Perception‐Native Electronic Skin: Bio‐Inspired In‐/Near‐Sensor and Neuromorphic Computing for Humanoid Robots

Advanced Materials Technologies, EarlyView.
Dense tactile streams from across the humanoid body converge on collide in a central wiring and data bottleneck. By relocating computation closer to and then into the skin itself, near‐ and in‐sensor architectures, together with neuromorphic computing, chart a path toward perception‐native electronic skin, in which the conversion of stimulus into ...
Mijin Kim, Chang‐Jae Beak, Jiyoung Park, Youngdo Jung, Sunjong Oh, Sin‐Hyung Lee, Bo‐Yeon Lee   +6 more
wiley   +1 more source