Search

Virtual metrology (VM) is crucial for improving process capability and production yield during semiconductor manufacturing processes. However, the performance of VM deteriorates owing to the variable operating regime and the nonlinear characteristics of the process. Herein, Variational inference Gaussian mixture model (VIGMM) and extreme learning machine (ELM) are combined to solve these issues. First, variational inference is conducted on a Gaussian mixture model to determine the number of Gaussian components automatically and the corresponding operating regimes are identified. Subsequently, an extreme learning machine is developed for each operating regime to investigate the nonlinear relationship between process inputs and outputs. Finally, VM is implemented using the corresponding local ELM, which is determined based on the responsibility of the Gaussian components. The feasibility and effectiveness of the proposed methods are validated based on a numerical case and the plasma sputtering process for fabricating thin-film transistor liquid-crystal displays. The proposed VIGMM-ELM can serve as a VM algorithm for manufacturing processes with multiple stages. [ABSTRACT FROM AUTHOR]

Distributed energy generation systems, key for producing electricity near usage points, are essential to meet theglobal electricity demand, leveraging diverse sources likerenewables, traditional fuels, and industrial waste heat. Despite theirhigh reliability, these systemsare not immune to faults and failures. Such incidents can result in considerable downtime and reduced efficiency, underlining the need for effectivefault detection and diagnosis techniques. Implementing these strategies is crucial not just for mitigating damage and preventing potential disasters, but also to maintain optimal performance levels. This paper introduces a novel methodology based on Bayesian graphical modeling for unsupervised fault diagnosis, focusing on organic Rankine cycle case study. It employsstructural learning to discern unknown intervention points within a directed acyclic graph that models the power plant’s operations. By analyzing real-world data, the study demonstrates the effectiveness of this approach, pinpointing a subset of variables that could be implicated in specific faults.

Target 3.4 of the third Sustainable Development Goal (SDG) of the United Nations (UN) General Assembly proposes to reduce premature mortality from non-communicable diseases (NCDs) by one-third. Epidemiological data presented by the World Health Organization (WHO) in 2016 show that out of a total of 57 million deaths worldwide, approximately 41 million deaths occurred due to NCDs, with 78% of such deaths occurring in low-and-middle-income countries (LMICs). The majority of investigations on NCDs agree that the leading risk factor for mortality worldwide is hypertension. Over 75% of the world's mobile phone subscriptions reside in LMICs, hence making the mobile phone particularly relevant to mHealth deployment in Africa. This study is aimed at determining the scope of the literature available on hypertension diagnosis and management in Africa, with particular emphasis on determining the feasibility, acceptability and effectiveness of interventions based on the use of mobile phones. The bulk of the evidence considered overwhelmingly shows that SMS technology is yet the most used medium for executing interventions in Africa. Consequently, the need to define novel and superior ways of providing effective and low-cost monitoring, diagnosis, and management of hypertension-related NCDs delivered through artificial intelligence and machine learning techniques is clear. [ABSTRACT FROM AUTHOR]