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Coronavirus 2019 (COVID-19) has posed a serious threat to the lives and health of the majority of people worldwide. Since the early days of the outbreak, South Korea's government and citizens have made persistent efforts to provide effective prevention against further spread of the disease. In particular, the participation of individual citizens in complying with the necessary code of conduct to prevent spread of the infection, through measures such as social distancing and mask wearing, is as instrumental as the geographical tracking of the trajectory of the infected. In this paper, we propose an activity recognition method based on a wristband equipped with an IR array and inertial measurement unit (IMU) to detect individual compliance with codes of personal hygiene management, such as mask wearing, which are recommended to prevent the spread of infectious diseases. The results of activity recognition were comparatively analyzed by applying conventional machine learning algorithms and convolutional neural networks (CNNs) to the IMU time series and IR array thermal images collected from 25 subjects. When CNN and 24 × 32 thermal images were used, 97.8% accuracy was achieved (best performance), and when 6 × 8 low-resolution thermal images were used, similar performance with 97.1% accuracy was obtained. In the case of using IMU, the performance of activity recognition was lower than that obtained with the IR array, but an accuracy of 93% was achieved even in the case of applying machine learning algorithms, indicating that it is more suitable for wearable devices with low computational capability. [ABSTRACT FROM AUTHOR]

In terms of electric vehicles (EVs), electric kickboards are crucial elements of smart transportation networks for short-distance travel that is risk-free, economical, and environmentally friendly. Forecasting the daily demand can improve the local service provider's access to information and help them manage their short-term supply more effectively. This study developed the forecasting model using real-time data and weather information from Jeju Island, South Korea. Cluster analysis under the rental pattern of the electric kickboard is a component of the forecasting processes. We cannot achieve noticeable results at first because of the low amount of training data. We require a lot of data to produce a solid prediction result. For the sake of the subsequent experimental procedure, we created synthetic time-series data using a generative adversarial networks (GAN) approach and combined the synthetic data with the original data. The outcomes have shown how the GAN-based synthetic data generation approach has the potential to enhance prediction accuracy. We employ an ensemble model to improve prediction results that cannot be achieved using a single regressor model. It is a weighted combination of several base regression models to one meta-regressor. To anticipate the daily demand in this study, we create an ensemble model by merging three separate base machine learning algorithms, namely CatBoost, Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The effectiveness of the suggested strategies was assessed using some evaluation indicators. The forecasting outcomes demonstrate that mixing synthetic data with original data improves the robustness of daily demand forecasting and outperforms other models by generating more agreeable values for suggested assessment measures. The outcomes further show that applying ensemble techniques can reasonably increase the forecasting model's accuracy for daily electric kickboard demand. [ABSTRACT FROM AUTHOR]

Automatic meter infrastructure (AMI) systems using remote metering are being widely used to utilize water resources efficiently and minimize non-revenue water. We propose a convolutional neural network-long short-term memory network (CNN-LSTM)-based solution that can predict faulty remote water meter reading (RWMR) devices by analyzing approximately 2,850,000 AMI data collected from 2762 customers over 360 days in a small-sized city in South Korea. The AMI data used in this study is a challenging, highly unbalanced real-world dataset with limited features. First, we perform extensive preprocessing steps and extract meaningful features for handling this challenging dataset with limited features. Next, we select important features that have a higher influence on the classifier using a recursive feature elimination method. Finally, we apply the CNN-LSTM model for predicting faulty RWMR devices. We also propose an efficient training method for ML models to learn the unbalanced real-world AMI dataset. A cost-effective threshold for evaluating the performance of ML models is proposed by considering the mispredictions of ML models as well as the cost. Our experimental results show that an F-measure of 0.82 and MCC of 0.83 are obtained when the CNN-LSTM model is used for prediction. [ABSTRACT FROM AUTHOR]