• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.6
• /
• pp.1478-1499
• /
• 2024

Coronavirus disease (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. COVID-19 become an active epidemic disease due to its spread around the globe. The main causes of the spread are through interaction and transmission of the droplets through coughing and sneezing. The spread can be minimized by isolating the susceptible patients. However, it necessitates remote monitoring to check the breathing issues of the patient remotely to minimize the interactions for spread minimization. Thus, in this article, we offer a wearable-IoTs-centered framework for remote monitoring and recognition of the breathing pattern and abnormal breath detection for timely providing the proper oxygen level required. We propose wearable sensors accelerometer and gyroscope-based breathing time-series data acquisition, temporal features extraction, and machine learning algorithms for pattern detection and abnormality identification. The sensors provide the data through Bluetooth and receive it at the server for further processing and recognition. We collect the six breathing patterns from the twenty subjects and each pattern is recorded for about five minutes. We match prediction accuracies of all machine learning models under study (i.e. Random forest, Gradient boosting tree, Decision tree, and K-nearest neighbor. Our results show that normal breathing and Bradypnea are the most correctly recognized breathing patterns. However, in some cases, algorithm recognizes kussmaul well also. Collectively, the classification outcomes of Random Forest and Gradient Boost Trees are better than the other two algorithms.

• Korean Journal of Remote Sensing
• /
• v.37 no.3
• /
• pp.503-514
• /
• 2021

Rice production with adequate level of area is important for decision making of rice supply and demand policy. It is essential to grasp rice cultivation areas in advance for estimating rice production of the year. This study was carried out to classify paddy rice cultivation in Gimje-si using sentinel-1 SAR (synthetic aperture radar) and UAV imagery in early July. Time-series Sentinel-1A and 1B images acquired from early May to early July were processed to convert into sigma naught (dB) images using SNAP (SeNtinel application platform, Version 8.0) toolbox provided by European Space Agency. Farm map and parcel map, which are spatial data of vector polygon, were used to stratify paddy field population for classifying rice paddy cultivation. To distinguish paddy rice from other crops grown in the paddy fields, we used the decision tree method using threshold levels and random forest model. Random forest model, trained by mainly rice cultivation area and rice and soybean cultivation area in UAV image area, showed the best performance as overall accuracy 89.9%, Kappa coefficient 0.774. Through this, we were able to confirm the possibility of early estimation of rice cultivation area in Gimje-si using UAV image.

• Korean Journal of Remote Sensing
• /
• v.23 no.6
• /
• pp.559-566
• /
• 2007

The generation of precise digital elevation model (DEM) is very important in coastal area where time series are especially required. Although a LIDAR system is useful in coastal regions, it is not yet popular in Korea mainly because of its high surveying cost and national security reasons. Recently, precise DEM has been made using radar interferometry and waterline methods. One of these methods, spaceborne imaging radar interferometry has been widely used to measure the topography and deformation of the Earth. We acquired ALOS PALSAR FBD mode (Fine Beam Dual) data for evaluating the quality of interferograms and their coherency. We attempted to construct DEM using ALOS PALSAR pairs - One pair is 2007/05/22 and 2007/08/22, another pair is 2007/08/22 and 2007/10/22 with respective perpendicular baseline of 820 m, 312m and respective height sensitivity of 75 m and 185m at southern of Ganghwa tidal flat, Siwha- and Hwaong-lake over west coastal of Korea peninsula. Ganghwa tidal flat has low coherence between 0.3 and 0.5 of 2007/05/22 and 2007/08/22 pair. However, Siwha-lake and Hwaong-lake areas have a higher coherence value (From 0.7 and 0.9) than Ganghwa tidal area. The reason of difference coherence value is tidal condition between tidal flat area (Ganghwa) and reclaimed zone (Siwha-lake and Hwaong-lake). Therefore, DEM was constructed by ALOS PALSAR pair over Siwha-lake and Hwaong-lake. If the temporal baseline is enough short to maintain the coherent phases and height sensitivity is enough small, we will be able to successfully construct a precise DEM over coastal area. From now on, more ALOS PALSAR data will be needed to construct precise DEM of West Coast of Korea peninsular.