MULTI-LEVEL K-d TREE-BASED DATA-DRIVEN COMPUTATIONAL METHOD FOR THE DYNAMIC ANALYSIS OF MULTI-MATERIAL STRUCTURES

2020 ◽  
Vol 18 (4) ◽  
pp. 421-438
Author(s):  
Zhangcheng Zheng ◽  
Hongfei Ye ◽  
Hongwu Zhang ◽  
Yonggang Zheng ◽  
Zhen Chen
Keyword(s):  
Dynamic Analysis ◽  
Computational Method ◽  
Data Driven ◽  
Multi Level

scholarly journals Data Driven Wireless Network Design: A Multi-level Modeling Approach

2016 ◽  
Vol 88 (1) ◽  
pp. 63-77
Author(s):  
Carolina Fortuna ◽  
Eli De Poorter ◽  
Primož Škraba ◽  
Ingrid Moerman
Keyword(s):  
Network Design ◽  
Wireless Network ◽  
Data Driven ◽  
Modeling Approach ◽  
Wireless Network Design ◽  
Multi Level

CSCORE: A SIMPLE YET EFFECTIVE SCORING FUNCTION FOR PROTEIN–LIGAND BINDING AFFINITY PREDICTION USING MODIFIED CMAC LEARNING ARCHITECTURE

2011 ◽  
Vol 09 (supp01) ◽  
pp. 1-14 ◽  
Author(s):  
XUCHANG OUYANG ◽  
STEPHANUS DANIEL HANDOKO ◽  
CHEE KEONG KWOH
Keyword(s):  
Binding Affinity ◽  
Scoring Function ◽  
Binding Mode ◽  
Computational Method ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Ligand Docking ◽  
Scoring Functions ◽  
Binding Affinity Prediction ◽  
Affinity Prediction

Protein–ligand docking is a computational method to identify the binding mode of a ligand and a target protein, and predict the corresponding binding affinity using a scoring function. This method has great value in drug design. After decades of development, scoring functions nowadays typically can identify the true binding mode, but the prediction of binding affinity still remains a major problem. Here we present CScore, a data-driven scoring function using a modified Cerebellar Model Articulation Controller (CMAC) learning architecture, for accurate binding affinity prediction. The performance of CScore in terms of correlation between predicted and experimental binding affinities is benchmarked under different validation approaches. CScore achieves a prediction with R = 0.7668 and RMSE = 1.4540 when tested on an independent dataset. To the best of our knowledge, this result outperforms other scoring functions tested on the same dataset. The performance of CScore varies on different clusters under the leave-cluster-out validation approach, but still achieves competitive result. Lastly, the target-specified CScore achieves an even better result with R = 0.8237 and RMSE = 1.0872, trained on a much smaller but more relevant dataset for each target. The large dataset of protein–ligand complexes structural information and advances of machine learning techniques enable the data-driven approach in binding affinity prediction. CScore is capable of accurate binding affinity prediction. It is also shown that CScore will perform better if sufficient and relevant data is presented. As there is growth of publicly available structural data, further improvement of this scoring scheme can be expected.

Multifractal Mice: Measuring Task Engagement and Readiness-to-hand via Hand Movement

2020 ◽  
Author(s):  
Daniel Bennett
Keyword(s):  
Hand Movement ◽  
Task Engagement ◽  
Computational Method ◽  
Data Driven ◽  
Prior Work ◽  
Wide Range ◽  
Input Signals ◽  
Data Driven Approach ◽  
Future Work ◽  
And Task

We introduce an unobtrusive, computational method for measuring readiness-to-hand and task-engagement during interaction."Readiness-to-hand" is an influential concept describing fluid, intuitive tool use, with attention on task rather than tool; it has longbeen significant in HCI research, most recently via metrics of tool-embodiment and immersion. We build on prior work in cognitivescience which relates readiness-to-hand and task engagement to multifractality: a measure of complexity in behaviour. We conduct areplication study (N=28), and two new experiments (N=44, N=30), which show that multifractality correlates with task-engagement and other features of readiness-to-hand overlooked in previous measures, including familiarity with task. This is the first evaluation of multifractal measures of behaviour in HCI. Since multifractality occurs in a wide range of behaviours and input signals, we support future work by sharing scripts and data (https://osf.io/2hm9u/), and introducing a new data-driven approach to parameter selection

scholarly journals Improved axial dynamic analysis of risers based on finite element method and data-driven models

2020 ◽  
Vol 214 ◽  
pp. 107782 ◽  
Author(s):  
Xiuquan Liu ◽  
Yanwei Li ◽  
Nan Zhang ◽  
Hexiang Sun ◽  
Yuanjiang Chang ◽  
...  
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Dynamic Analysis ◽  
Data Driven ◽  
Element Method

scholarly journals Data-driven energy analysis of supermarkets: a multi-level approach for different stakeholders

Procedia CIRP ◽  
2021 ◽  
Vol 98 ◽  
pp. 61-66
Author(s):  
Benjamin Uhlig ◽  
Christine Blume ◽  
Sebastian Thiede ◽  
Mark Mennenga ◽  
Christoph Herrmann
Keyword(s):  
Energy Analysis ◽  
Data Driven ◽  
Multi Level
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