Ethanol poses a significant threat to driver safety, as its effects vary with blood alcohol concentration (BAC). Common methods for estimating BAC include breath alcohol analysis, which calculates BAC from the alcohol concentration in exhaled breath, and direct blood sampling. However, these methods have notable limitations. This study aims to classify alcohol concentration using non-invasive optical signal data obtained from biomimetic samples with varying alcohol levels. To replicate the high scattering characteristics of biological tissue, scattering effects were induced in the samples, and absorbance was measured using near-infrared (NIR) wavelengths, which penetrate biological tissue more deeply. A Random Forest (RF) model was trained using the measured absorbance values to classify alcohol concentration levels. The Area Under the ROC Curve (AUC) for each concentration level indicated effective model learning, and the classification results on the test set demonstrated statistically significant accuracy. These findings suggest that the RF model can classify alcohol concentrations non-invasively and without the loss of samples. Furthermore, incorporating additional optical properties beyond absorbance may improve the accuracy of future non-invasive alcohol concentration classification models.

The purpose of this study was to check the position classification prediction rate based on the movement data of field hockey players using the random forest algorithm. In order to achieve the purpose of this study, movement data were collected using wearable devices in 15 practice matches. The collected information was then analyzed using the Random Forest algorithm, one of the ensemble techniques, with Python, a high-level, general-purpose programming language. As a result of this study, first, the position classification prediction rate was 52.4±3.3% when data measured by GPS sensors were used. Second, when using the data measured by an inertial measurement unit (IMU) sensor, the position classification prediction rate was 50.8±2.4%. Third, when both Global Positioning System (GPS) and IMU data were used, the position classification prediction rate was 55.6±2.0%. As a result of the study, it showed that the prediction rate was the highest when both GPS and IMU data were used.

A smart factory with Big Data analytics is getting attention because of its ability to automate and make the manufacturing environment more intelligent. At the same time, higher reliability is required with a drastic increase in complexity and uncertainty within the current system of manufacturing fields. The pump is considered as one of the most crucial equipment as it can affect the overall manufacturing performance of the manufacturing processes and it needs to be timely diagnosed of its mechanical condition as a top priority. In this research, we propose an operation system of centrifugal pumps and a data-driven fault diagnostic model that is developed by collecting relevant multivariate data from several natures. Proposed machine learning models can be used for detecting and diagnosing pump faults via analytical processes containing signal preprocessing and feature engineering procedures. Simulation and case studies from rotating machinery have demonstrated the effectiveness of the proposed analytical framework not only for attaining quantitative reliability but practical usages in actual manufacturing fields as well.