Growing competition among manufacturing businesses and the advent of the Fourth Industrial Revolution has meant that many countries are conducting various research projects to understand how to introduce and populate smart factories. Smart factories are expected to provide a way of solving the manufacturing industries’ complex problems, to take a role in breakthroughs in factories and to carry on a sustainable business. Smart factories are currently in the introduction stage, so we should follow up on the majorities and check their tendencies. However, smart-factory research is an interdisciplinary field that should be studied by researchers with diverse backgrounds in various domains. Thus, studying the past and present overall research trends of smart factory studies is required for their successful introduction and sustainable research. In this study, we explored the research trends of smart factories in both international and specifically Korean research, as an example of a nation case, to determine the major research directions. We determined trends using latent semantic analysis, which is a known topic-modeling technique, and analyzed the trends with regression-based methods. As a result, we could read the clear trends by analyzing existing studies related to smart factories. In addition, it is possible to compare research trends in Korea and international research trends for the commonly appeared topics, such as ‘ICT’ (Information and Communications Technology) and ‘R&D (Research and Development)/Technology Innovation’. We expect that the quantitative analysis results and suggestions presented in this study can be used to formulate strategies for the future diffusion of smart factories.
As unmanned aerial vehicles have become popular, the number of accidents caused by an operator’s inattention have increased. To prevent such accidents, the operator should maintain an attention status. However, limited research has been conducted on the brain-computer interface (BCI)-based system with an alerting module for the operator’s attention recovery of unmanned aerial vehicles. Therefore, we introduce a detection and alerting system that prevents an unmanned aerial vehicle operator from falling into inattention status by using the operator’s electroencephalogram signal. The proposed system consists of the following three components: a signal processing module, which collects and preprocesses an electroencephalogram signal of an operator, an inattention detection module, which determines whether an inattention status occurred based on the preprocessed signal, and, lastly, an alert providing module that presents stimulus to an operator when inattention is detected. As a result of evaluating the performance with a real-world dataset, it was shown that the proposed system successfully contributed to the recovery of operator attention in the evaluating dataset, although statistical significance could not be established due to the small number of subjects.
As language editing became an essential process for enhancing the quality of a research manuscript, there are several companies providing manuscript editing services. In such companies, a manuscript submitted for proofreading is matched with an editing expert through a manual process, which is costly and often subjective. The major drawback of the manual process is that it is almost impossible to consider the inherent characteristics of a manuscript such as writing style and paragraph composition. To this end, we propose an expert recommendation method for manuscript editing services based on matrix factorization, a well-known collaborative filtering approach for learning latent information in ordinal ratings given by users. Specifically, binary ratings are utilized to substitute ordinal ratings when negative opinions are expressed by users since negative opinions are more accurately expressed by binary ratings than ordinal ratings. From the experiments using a real-world dataset, the proposed method outperformed the rest of the compared methods with an RMSE (root mean squared error) of 0.1. Moreover, the effectiveness of substituting ordinal ratings with binary ratings was validated by conducting sentiment analysis on text reviews.
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