This paper aims to prove the validity and necessity of knowledge stickiness and knowledge investment level.
Abstract
Purpose
This paper aims to prove the validity and necessity of knowledge stickiness and knowledge investment level.
Design/methodology/approach
Empirical study method is taken in this investigation which focuses on knowledge‐related industries' workers and proves the validity and necessity of knowledge stickiness and knowledge investment level with SPSS13.0 software.
Findings
The authors confirm the positive correlation between knowledge contribution and sharing residual claims based on management, and also confirm the positive correlation between knowledge stickiness, knowledge investment level and sharing residual claims based on technology. However, a negative correlation on management is also confirmed.
Originality/value
After an analysis on the incentive distortion caused by the information asymmetry between the principal and agent in the traditional incentive mode, a residual claims sharing structure containing knowledge contract is put forward.
Details
Keywords
Umair Khan, William Pao, Karl Ezra Salgado Pilario, Nabihah Sallih and Muhammad Rehan Khan
Identifying the flow regime is a prerequisite for accurately modeling two-phase flow. This paper aims to introduce a comprehensive data-driven workflow for flow regime…
Abstract
Purpose
Identifying the flow regime is a prerequisite for accurately modeling two-phase flow. This paper aims to introduce a comprehensive data-driven workflow for flow regime identification.
Design/methodology/approach
A numerical two-phase flow model was validated against experimental data and was used to generate dynamic pressure signals for three different flow regimes. First, four distinct methods were used for feature extraction: discrete wavelet transform (DWT), empirical mode decomposition, power spectral density and the time series analysis method. Kernel Fisher discriminant analysis (KFDA) was used to simultaneously perform dimensionality reduction and machine learning (ML) classification for each set of features. Finally, the Shapley additive explanations (SHAP) method was applied to make the workflow explainable.
Findings
The results highlighted that the DWT + KFDA method exhibited the highest testing and training accuracy at 95.2% and 88.8%, respectively. Results also include a virtual flow regime map to facilitate the visualization of features in two dimension. Finally, SHAP analysis showed that minimum and maximum values extracted at the fourth and second signal decomposition levels of DWT are the best flow-distinguishing features.
Practical implications
This workflow can be applied to opaque pipes fitted with pressure sensors to achieve flow assurance and automatic monitoring of two-phase flow occurring in many process industries.
Originality/value
This paper presents a novel flow regime identification method by fusing dynamic pressure measurements with ML techniques. The authors’ novel DWT + KFDA method demonstrates superior performance for flow regime identification with explainability.