Results 101 to 110 of about 448,585 (316)

Loss and Risk in Smoothing Parameter Selection [PDF]

, 1992
1 online resource (PDF, 43 pages)Grund, Birgit; Hall, Peter; Marron, J. S.. (1992). Loss and Risk in Smoothing Parameter Selection.
Grund, Birgit, Hall, Peter, Marron, J. S.   +2 more
core  

Productivity‐Driven Optimization of Laser Powder Bed Fusion Parameters for IN718 Superalloy: Process Control, Microstructure, and Mechanical Properties

Advanced Engineering Materials, EarlyView.
This study demonstrates how optimizing laser power, scanning speed, and hatching distance in laser powder bed fusion can boost the productivity of Inconel 718 manufacturing by up to 29% while maintaining mechanical integrity. The work delivers a validated process window and cost–time analysis, offering industry‐ready guidelines for efficient additive ...
Amir Behjat, Guido Barale, John Stavridis, Ehsan Norouzi, Jin‐Yoo Suh, Dong‐Ik Kim, Luca Iuliano, Abdollah Saboori   +7 more
wiley   +1 more source

Multi-source data fusion-based knowledge transfer for unmanned aerial vehicle flight data anomaly detection and recovery

Scientific Reports
Flight data anomaly detection (AD) is essential for unmanned aerial vehicle (UAV) health management. Despite the current dominance of data-driven approaches, their effectiveness often requires sufficient data for model training.
Lei Yang, Shaobo Li, Liya Yu, Caichao Zhu, Congbao Wang   +4 more
doaj   +1 more source

Empirical Information Criteria for Time Series Forecasting Model Selection [PDF]

In this paper, we propose a new Empirical Information Criterion (EIC) for model selection which penalizes the likelihood of the data by a function of the number of parameters in the model.
R.J. Hyndman, Md B. Billah, A.B. Koehler
core  

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 Novel Hierarchical Clustering Algorithm Based on Density Peaks for Complex Datasets

Complexity, 2018
Clustering aims to differentiate objects from different groups (clusters) by similarities or distances between pairs of objects. Numerous clustering algorithms have been proposed to investigate what factors constitute a cluster and how to efficiently ...
Rong Zhou, Yong Zhang, Shengzhong Feng, Nurbol Luktarhan   +3 more
doaj   +1 more source

Optimized LOWESS normalization parameter selection for DNA microarray data

BMC Bioinformatics, 2004
Background Microarray data normalization is an important step for obtaining data that are reliable and usable for subsequent analysis. One of the most commonly utilized normalization techniques is the locally weighted scatterplot smoothing (LOWESS ...
Edgren Henrik, Järvinen Anna-Kaarina, Hautaniemi Sampsa, Berger John A, Mitra Sanjit K, Astola Jaakko   +5 more
doaj   +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

npbr: A Package for Nonparametric Boundary Regression in R

Journal of Statistical Software, 2017
The package npbr is the first free specialized software for data edge and frontier analysis in the statistical literature. It provides a variety of functions for the best known and most innovative approaches to nonparametric boundary estimation.
Abdelaati Daouia, Thibault Laurent, Hohsuk Noh   +2 more
doaj   +1 more source

Bias in nearest-neighbor hazard estimation [PDF]

In nonparametric curve estimation, the smoothing parameter is critical for performance. In order to estimate the hazard rate, we compare nearest neighbor selectors that minimize the quadratic, the Kullback-Leibler, and the uniform loss.
Weißbach, Rafael, Dette, Holger
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