Process monitoring based on orthogonal locality preserving projection with maximum likelihood estimation

By integrating two powerful methods of density reduction and intrinsic dimensionality estimation, a new data-driven method, referred to as OLPP-MLE (orthogonal locality preserving projection-maximum likelihood estimation), is introduced for process monitoring. OLPP is utilized for dimensionality reduction, which provides better locality preserving power than locality preserving projection. Then, the MLE is adopted to estimate intrinsic dimensionality of OLPP. Within the proposed OLPP-MLE, two new static measures for fault detection TOLPP2 and SPEOLPP are defined. In order to reduce algorithm complexity and ignore data distribution, kernel density estimation is employed to compute thresholds for fault diagnosis. The effectiveness of the proposed method is demonstrated by three case studies

Graph-based prediction of missing KPIs through optimization and random forests for KPI systems

Key performance indicators (KPIs) are widely used to monitor and control the production in industry. On an aggregated level, often represented as graphs or interrelated KPI systems, a comprehensive overview is given. However, missing or inaccurate sensor data and KPIs, as well inconsistencies in KPI based management are a major hurdle disturbing operations. To counter the impact of such missing KPIs, we propose a value optimization based approach to reconstruct the values of missing KPIs within a KPI system. While the approach shows successful reconstruction in the case study, the value optimization can be sped up through a random forest prediction of the initial optimization set. Thus, the inclusion of previous knowledge about the system behavior proves beneficial and superior to the pure optimization based approach, as validated by both randomized and simulation-based measurement data

An advanced PLS approach for key performance indicator-related prediction and diagnosis in case of outliers

In recent papers [1,2], two new ways have been proposed to probe the linear polarization of gluons in unpolarized proton: using the azimuthal asymmetries and Callan-Gross ratio in heavy-quark pair leptoproduction, $lN\rightarrow l^{\prime}Q\bar{Q}X$. In this talk, we discuss in details the sensitivity of the QCD predictions for the azimuthal $\cos \varphi$ and $\cos 2\varphi$ asymmetries to the contribution of linearly polarized gluons inside unpolarized proton, where the azimuth $\varphi$ is the angle between the lepton scattering plane $(l,l^{\prime})$ and the heavy quark production plane $(N,Q)$. Our analysis shows that the azimuthal distributions under consideration vary from 0 to 1 depending on the transverse-momentum dependent gluonic counterpart of the Boer-Mulders function, $h_{1}^{\perp g}$. We conclude that the $\cos \varphi$ and $\cos 2\varphi$ asymmetries in heavy-quark pair production in DIS processes are predicted to be large in wide kinematic ranges and sensitive to the contribution of linearly polarized gluons.Comment: 6 pages. Contribution to Baldin Seminar ISHEPP XXIV, Dubna, Russia, September 17-22, 2018. arXiv admin note: text overlap with arXiv:1711.0522

Factors affecting cryptocurrency adoption in digital market of Malaysia

Cryptocurrency plays an important role in today's digital currency environment. Improving cryptocurrency adoption is important for consumers and practitioners, as it improves understanding, enhances behavior, attitude, trust, and increases satisfaction. Though the lack of cryptocurrency adoption is a significant issue that arises in the digital market, cryptocurrency adoption is crucial to the support of technology capability facilitated with appropriate behavioral intention too. Considering the fact, this study intended to investigate the impact of cryptocurrency adoption in the digital market in Malaysia. This empirical study examined the role of trust (TR), social influence (SI), cryptocurrency transaction transparency (CTT), technology awareness (TA), facilitating conditions (FC), performance expectancy (PE), attitude (AT), customer satisfaction on behavioral intention (BI) and cryptocurrency adoption (CA). The study also intended to examine the role of behavioral intention as a mediator in the context of cryptocurrency adoption. In line with the research objectives, systematic random sampling was used in this study. Cross-sectional data were collected using a questionnaire at the cryptocurrency consumer of Malaysia, which produced a total of 349 usable responses. The study employed Partial Least Squares Structural Equation Modelling (PLS-SEM) for data analysis. Findings o the study revealed that TR, SI, CTT, TA, and FC positively affect CA (dependent variable) through the mediation of behavioral intention (BI) in Malaysia's digital market. On the other hand, PE, AT, and CS negatively affect cryptocurrency adoption in Malaysia's digital market. Future researchers may replicate the study in different countries in a different industry context and integrate similar constructs to broaden the current body of knowledge