Fault Feature Extraction and Diagnosis of Gearbox Based on EEMD and Deep Briefs Network by Kai Chen, Xin-Cong Zhou, Jun-Qiang Fang, Peng-fei Zheng, Jun Wang

“…In this paper, a novel fault diagnosis method based on ensemble empirical mode decomposition (EEMD) and Deep Briefs Network (DBN) is proposed to treat the vibration signals measured from gearbox. …”
Get full text

Prediction of Sonic Log Values Using a Gradient Boosting Algorithm in the 'AB' Field by Rasif Nahari, Utama Widya, Ardhya Garini Sherly, Fitri Indriani Rista, Pratama Novian Putra Dhea

“…Gradient boosting employs an ensemble of decision trees, iteratively correcting errors to model complex data patterns. …”
Get full text

La réduplication à valeur d'insistance dans une grammaire accueillant la langue des conversations familières by Gaétane Dostie

“…On s’attarde : - aux diverses sortes de réduplication discutées dans la littérature sur le sujet et à la place occupée par la RPI dans ce vaste ensemble ; - à la valeur modale de la RPI et à son lien avec les actes de langage ; - à sa valeur iconique ; - aux traits sémantiques généraux associés aux mots usuellement pragmatiquement rédupliqués dans les conversations familières ; - au poids interactionnel rattaché à certains cas récurrents de RPI eu égard à des stratégies en lien avec la politesse linguistique. …”
Get full text

The collective burials of Tablada de Lurín, Lurín valley, Peru (AD 1-300) by Karina Gerdau-Radonic

“…Les individus étaient assis/accroupis dans des paniers, l’ensemble enveloppé dans un linceul. Cette position d’ensevelissement facilite des observations quant à l’ordre dans lequel disparaissent certaines connexions anatomiques pendant la décomposition du cadavre.…”
Get full text

Analysing the Effects of Atmospheric Teleconnections on Streamflow Regime in the Eastern Black Sea Basin in Türkiye by Cenk Sezen

“…For this purpose, Spearman’s correlation test, ensemble empirical mode decomposition (EEMD) and relative importance analysis were used. …”
Get full text

Direct Numerical Simulations of the Impact of High Turbulence Intensities and Volume Viscosity on Premixed Methane Flames by Gordon Fru, Gábor Janiga, Dominique Thévenin

“…Flame thickening is observed to be predominant at high turbulent Reynolds number. Via ensemble averaging, it is shown that both laminar and turbulent flame structures are not modified by κ. …”
Get full text

Mémoire et Denkraum. Réflexions épistémologiques sur la Kulturwissenschaftliche Bibliothek Warburg by Maud Hagelstein

“…On connaît généralement cette bibliothèque grâce au témoignage d’Ernst Cassirer qui voyait s’y déployer un « ensemble de problèmes ». Le philosophe ne cache pas l’angoisse qu’il ressentit d’abord face à ce labyrinthe de livres dont l’organisation lui échappait. …”
Get full text

Forecasting Shifts in Europe's Renewable and Fossil Fuel Markets Using Deep Learning Methods by Yonghong Liu, Muhammad S. Saleem, Javed Rashid, Sajjad Ahmad, Muhammad Faheem

“…The comparison of our model (Bi‐GRU) performance with other popular models, including bidirectional long short‐term memory (Bi‐LSTM), ensemble techniques combining convolutional neural networks (CNN) and Bi‐LSTM, and CNNs, make the study more interesting. …”
Get full text

Peptidomic analysis of whey protein hydrolysates and prediction of their antioxidant peptides by Jesus Morales García, Chibuike C. Udenigwe, Jorge Duitama, Andrés Fernando González Barrios

“…We also developed an ensemble machine learning predictor to classify peptides obtained from whey hydrolysis. …”
Get full text

Outlier Detection and Correction for Monitoring Data of Water Quality Based on Improved VMD and LSSVM by Guangpei Sun, Peng Jiang, Huan Xu, Shanen Yu, Dong Guo, Guang Lin, Hui Wu

“…The result shows that the improved VMD algorithm is of higher detection rate and lower error rate than those of Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD). The LSSVM algorithm increases the fitting accuracy and decreases correction error in comparison with SVM and BP neural network, which provides important references for the implementation of environmental protection measures.…”
Get full text

Lubrication State Recognition Based on Energy Characteristics of Friction Vibration with EEMD and SVM by Hai-jie Yu, Hai-jun Wei, Jing-ming Li, Da‐ping Zhou, Li‐dui Wei, Hong Liu

“…Based on this, a lubrication state recognition method with ensemble empirical mode decomposition (EEMD) and support vector machine (SVM) was proposed. …”
Get full text

Credit risk prediction with corruption perception index: machine learning approaches by Cuong Nguyen Thanh, Tam Phan Huy, Tuyet Pham Hong, An Bui Nguyen Quoc

“…Findings indicate that ensemble methods, particularly XGBoost, outperform simpler models in predicting NPLs influenced by corruption. …”
Get full text

Diagnostic classification in toxicologic pathology using attention-guided weak supervision and whole slide image features: a pilot study in rat livers by Philip Zehnder, Jeffrey Feng, Trung Nguyen, Philip Shen, Ruth Sullivan, Reina N. Fuji, Fangyao Hu

“…We also demonstrate how an ensemble of binary classifiers improves interpretability and allows for multiclass classification and the classification of diagnostic regions of interest in each slide. …”
Get full text

›Lead Sheet‹-Notation vom Vaudeville bis zum iReal Pro. Zur Geschichte der Notationsformen des Jazz und ihren musiktheoretischen Implikationen by Philipp Teriete

“…It is also expected that they bring their own compositions in a lead sheet format to ensemble rehearsals and concerts to facilitate performance and improvisation. …”
Get full text

Shear Strength of Internal Reinforced Concrete Beam-Column Joints: Intelligent Modeling Approach and Sensitivity Analysis by De-Cheng Feng, Bo Fu

“…The proposed approach is established based on the famous boosting-family ensemble machine learning (ML) algorithms, i.e., gradient boosting regression tree (GBRT), which generates a strong predictive model by integrating several weak predictors, which are obtained by the well-known individual ML algorithms, e.g., DT, ANN, and SVM. …”
Get full text

Recent advances in journal bearings: wear fault diagnostics, condition monitoring and fault diagnosis methodologies by Nazik Jebur, Wafa Soud

“…Key findings indicate that ensemble models, such as the CNN and deep neural network (CNNEPDNN) model, significantly improve convergence speed, test accuracy, and F-Score in bearing fault diagnosis by 15-20% compared to individual models. …”
Get full text

Enhancing adversarial transferability with local transformation by Yang Zhang, Jinbang Hong, Qing Bai, Haifeng Liang, Peican Zhu, Qun Song

“…Particularly, in the case of ensemble attacks, our method achieved an average attack success rate of 98.3%.…”
Get full text

Space-Based FPGA Radio Receiver Design, Debug, and Development of a Radiation-Tolerant Computing System by Zachary K. Baker, Mark E. Dunham, Keith Morgan, Michael Pigue, Matthew Stettler, Paul Graham, Eric N. Schmierer, John Power

“…Due to our focus on satellite applications, each design must extract the best size, weight, and power performance possible from the ensemble of Commodity Off-the-Shelf (COTS) parts available at the time of design. …”
Get full text

Comprehensive evaluation of seasonal forecasts from NMME and statistical models over the Blue Nile Basin and the Grand Ethiopian Renaissance Dam (GERD) watershed by Mekonnen Gebremichael, Yu-Chuan Tien, Vahid Nourani

“…Study region: The Blue Nile basin, including the Grand Ethiopian Renaissance Dam (GERD) Study focus: This study evaluates the performance of seasonal precipitation forecasts from the North American Multi-Model Ensemble (NMME) over the Blue Nile Basin and compares them to newly developed statistical forecasting models, using the satellite-based Integrated Multi-satellitE Retrievals for GPM (IMERG) as the reference product. …”
Get full text

Bulk Low-Inertia Power Systems Adaptive Fault Type Classification Method Based on Machine Learning and Phasor Measurement Units Data by Mihail Senyuk, Svetlana Beryozkina, Inga Zicmane, Murodbek Safaraliev, Viktor Klassen, Firuz Kamalov

“…To enhance the speed and accuracy of disturbance classification, we employ a range of ensemble machine learning techniques, including Random forest, AdaBoost, Extreme gradient boosting (XGBoost), and LightGBM. …”
Get full text