Results 51 to 60 of about 128,875 (283)

Log-based Anomaly Detection of CPS Using a Statistical Method

, 2017
Detecting anomalies of a cyber physical system (CPS), which is a complex system consisting of both physical and software parts, is important because a CPS often operates autonomously in an unpredictable environment.
Choi, Eun-Hye, Harada, Yoshiyuki, Mizuno, Osamu, Yamagata, Yoriyuki   +3 more
core   +1 more source

Clustering Algorithm Reveals Dopamine‐Motor Mismatch in Cognitively Preserved Parkinson's Disease

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To explore the relationship between dopaminergic denervation and motor impairment in two de novo Parkinson's disease (PD) cohorts. Methods n = 249 PD patients from Parkinson's Progression Markers Initiative (PPMI) and n = 84 from an external clinical cohort.
Rachele Malito, Chiara Meneghini, Alice Galli, Luca Gallo, Pierfrancesco Mitrotti, Paola Dimartino, Marianna Inglese, Cinzia Zatti, Alessandro Lupini, Andrea Pilotto, Alessandro Padovani, Micol Avenali, Enza Maria Valente, Cristina Tassorelli, Silvia P. Caminiti   +14 more
wiley   +1 more source

Crowdsourced correlation clustering with relative distance comparisons

, 2017
Crowdsourced, or human computation based clustering algorithms usually rely on relative distance comparisons, as these are easier to elicit from human workers than absolute distance information.
Ukkonen, Antti
core   +1 more source

Complementarity of Long‐Reads and Optical Mapping in Parkinson's Disease for Structural Variants

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Long‐read sequencing and optical genome mapping technologies have the ability to detect large and complex structural variants. This has led to the discovery of novel pathogenic variants in neurodegenerative movement disorders. Thus, we aimed to systematically compare the SV detection capabilities of OGM and ONT in Parkinson's disease.
André Fienemann, Theresa Lüth, Susen Schaake, Carolin Gabbert, Marius Möller, Hauke Busch, Katja Lohmann, Jonas A. Gustafson, Danny E. Miller, Kensuke Daida, Manabu Funayama, Nobutaka Hattori, Samia Ben Sassi, Faycel Hentati, Matthew J. Farrer, Kristian K. Ullrich, Christine Klein, Joanne Trinh   +17 more
wiley   +1 more source

Hopfield Neural Networks for Online Constrained Parameter Estimation With Time‐Varying Dynamics and Disturbances

International Journal of Adaptive Control and Signal Processing, EarlyView.
This paper proposes two projector‐based Hopfield neural network (HNN) estimators for online, constrained parameter estimation under time‐varying data, additive disturbances, and slowly drifting physical parameters. The first is a constraint‐aware HNN that enforces linear equalities and inequalities (via slack neurons) and continuously tracks the ...
Miguel Pedro Silva
wiley   +1 more source

HLoOP—Hyperbolic 2-Space Local Outlier Probabilities

IEEE Access
Hyperbolic geometry has recently garnered considerable attention in machine learning due to its ability to embed hierarchical graph structures with low distortions for further downstream processing.
Clemence Allietta, Jean-Philippe Condomines, Jean-Yves Tourneret, Emmanuel Lochin   +3 more
doaj   +1 more source

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

Adaptable and Robust EEG Bad Channel Detection Using Local Outlier Factor (LOF)

Italian National Conference on Sensors, 2022
Electroencephalogram (EEG) data are typically affected by artifacts. The detection and removal of bad channels (i.e., with poor signal-to-noise ratio) is a crucial initial step.
V. Kumaravel, M. Buiatti, Eugenio Parise, Elisabetta Farella   +3 more
semanticscholar   +1 more source

A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions

Advanced Engineering Materials, EarlyView.
This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice, Krzysztof S. Stopka, Michael D. Sangid   +2 more
wiley   +1 more source

Anomaly detection and clustering‐based identification method for consumer–transformer relationship and associated phase in low‐voltage distribution systems

Energy Conversion and Economics, 2022
The identification accuracy of low‐voltage distribution consumer–transformer relationship and phase are crucial to three‐phase unbalanced regulation and error correction in consumer–transformer relationships.
Zhenyue Chu, Xueyuan Cui, Xingli Zhai, Shengyuan Liu, Weiqiang Qiu, Muhammad Waseem, Tarique Aziz, Qin Wang, Zhenzhi Lin   +8 more
doaj   +1 more source