• Journal of Korea Society of Industrial Information Systems
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• v.15 no.2
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• pp.91-98
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• 2010

This study proposes a monitoring system that effectively watches surroundings by cooperating the various sensor information including image information on a sensor network system. The monitoring system proposed in this paper is developed to watch certain intruders to the internal spaces through the interested region for exceptional time by installing cameras, PIR(Pyroelectric Infrared Ray) sensor and body detectors in such interested regions. Moreover the monitering system is implemented based on the SenWeaver plateform which is a integrated development tools for building wireless sensor network system. In the results of the test that was applied to a practically experimental environment by implementing some interfaces for the proposed system, it was considered that it is possible to watch surroundings effectively using the image information obtained from cameras and multiple sensor information acquisited from sensor nodes.

• Journal of Advanced Navigation Technology
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• v.27 no.3
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• pp.262-268
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• 2023

Based on the government's willingness to commercialize UAM with the goal of 2025, it is making remarkable achievements in various fields, including the development of UAM. In addition, based on the concept of UAM, it is evolving into an Advanced Air Mobility(AAM) concept that includes commercial operation between long-distance or short-range cities, cargo delivery, public services, aviation tourism, and personal/leisure aircraft. however, research on physical problems such as low-altitude operation characteristics, speed within three dimensions, and dizziness caused by external environment has yet to be found. Therefore, in this study, actual images are taken while flying at the expected altitude and speed of UAM using a helicopter, and by experiencing it to the general public using a UAM simulator equipped with VR and Motion, physical reactions such as fear and dizziness of passengers that may occur during actual UAM operation of UAM are analyzed.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.3
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• pp.57-67
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• 2023

A flash drought is a rapid-onset drought that develops over a short period of time as weather and environmental factors change rapidly, unlike general droughts, due to meteorological abnormalities. Abnormally high evapotranspiration rates and rapid declines in soil moisture increase vegetation stress. In addition, crop yields may decrease due to flash droughts during crop growth and may damage agricultural and economic ecosystems. In this study, Flash Drought Intensity Index (FDII) based on soil moisture data from Gravity Recovery Climate Experiment (GRACE) was used to analyze flash drought. FDII, which is calculated using soil moisture percentile, is expressed by multiplying two factors: the rate of intensification and the drought severity. FDII was developed for domestic flash drought events from 2014 to 2018. The flash drought that occurred in 2018, Chungcheongbuk-do showed the highest FDII. FDII was higher in heat wave flash drought than in precipitation deficit flash drought. The results of this study show that FDII is reliable flash drought analysis tool and can be applied to quantitatively analyze the characteristics of flash drought in South Korea.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.187-195
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• 2023

This study propose to develop artificial intelligence liberal arts courses for college students in the humanities and social sciences majors using the entry artificial intelligence model. A group of experts in computer, artificial intelligence, and pedagogy was formed, and the final artificial intelligence liberal arts course was developed using previous research analysis and Delphi techniques. As a result of the study, the educational topics were largely composed of four categories: image classification, image recognition, text classification, and sound classification. The training consisted of 1) Understanding the principles of artificial intelligence, 2) Practice using the entry artificial intelligence model, 3) Identifying the Ethical Impact, and 4) Based on learned, team idea meeting to solve real-life problems. Through this course, understanding the principles of the core technology of artificial intelligence can be directly implemented through the entry artificial intelligence model, and furthermore, based on the experience of solving various real-life problems with artificial intelligence, and it can be expected to contribute positively to understanding technology, exploring the ethics needed in the artificial intelligence era.

• The Journal of Engineering Geology
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• v.33 no.4
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• pp.689-699
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• 2023

Assessing ground stability is critical to the construction of underground transportation infrastructure. Surface displacement is a key indicator of ground stability, and can be measured using interferometric synthetic aperture radar (InSAR). This study measured time-series surface displacement using permanent scatterer InSAR applied to Sentinel-1 SAR images acquired from January 2017 to June 2023 for the area around a deep underground expressway under construction to connect Mandeok-dong and Centum City in Busan, South Korea. Regions of seasonal subsidence and uplift were identified, as were regions with severe subsidence after summer 2022. To evaluate stability of the ground in the construction area, the mean displacement velocity, final surface displacement, cumulative surface displacement, and difference between minimum and maximum surface displacement were analyzed. Considering the time-series surface displacement characteristics of the study area, the difference between minimum and maximum surface displacement since June 2022 was found to be the most suitable parameter for evaluating ground stability. The results identified highly unstable ground in the construction area as being to the north of the mid-lower reaches of the Oncheon-cheon River and to the west of the Suyeong River at the point where both rivers meet, with the difference between minimum and maximum surface displacement of 40~60 mm.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.103-108
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• 2024

In this paper, a smart elevator system was studied using real-time object detection technology based on YOLO(You only look once)v5. When an external elevator button is pressed, the YOLOv5 model analyzes the camera video to determine whether there are people waiting, and if it determines that there are no people waiting, the button is automatically canceled. The study introduces an effective method of implementing object detection and communication technology through YOLOv5 and MQTT (Message Queuing Telemetry Transport) used in the Internet of Things. And using this, we implemented a smart elevator system that determines in real time whether there are people waiting. The proposed system can play the role of CCTV (closed-circuit television) while reducing unnecessary power consumption. Therefore, the proposed smart elevator system is expected to contribute to safety and security issues.

• Journal of the Korea Society of Computer and Information
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• v.29 no.6
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• pp.211-221
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• 2024

Live streaming commerce has emerged as an innovative e-commerce model. This study, based on the Elaboration Likelihood Model (ELM), aims to explore the impact of Key Opinion Consumers' (KOCs) attributes in live streaming commerce on purchase intentions on short video platforms. A survey was conducted with 411 consumers, and data analysis and hypothesis testing were performed using SPSS 24.0 and AMOS 23.0 software. Research has found that differences in consumers' information processing abilities lead to different pathway selections. Central route factors such as recommendation consistency, product involvement, and professionalism, as well as peripheral route factors such as recommendation timeliness, all have significant positive effects on consumers' purchase intention. However, visual cues in the peripheral route do not have a significant impact. This study aims to provide theoretical support and practical guidance for the development of the live streaming commerce industry, and to help companies adjust their promotion strategies based on differences in consumer information processing, thereby improving purchase conversion rates.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1037-1051
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• 2020

The accurate monitoring and forecasting of the intensity of tropical cyclones (TCs) are able to effectively reduce the overall costs of disaster management. In this study, we proposed a multi-task learning (MTL) based deep learning model for real-time TC intensity estimation and forecasting with the lead time of 6-12 hours following the event, based on the fusion of geostationary satellite images and numerical forecast model output. A total of 142 TCs which developed in the Northwest Pacific from 2011 to 2016 were used in this study. The Communications system, the Ocean and Meteorological Satellite (COMS) Meteorological Imager (MI) data were used to extract the images of typhoons, and the Climate Forecast System version 2 (CFSv2) provided by the National Center of Environmental Prediction (NCEP) was employed to extract air and ocean forecasting data. This study suggested two schemes with different input variables to the MTL models. Scheme 1 used only satellite-based input data while scheme 2 used both satellite images and numerical forecast modeling. As a result of real-time TC intensity estimation, Both schemes exhibited similar performance. For TC intensity forecasting with the lead time of 6 and 12 hours, scheme 2 improved the performance by 13% and 16%, respectively, in terms of the root mean squared error (RMSE) when compared to scheme 1. Relative root mean squared errors(rRMSE) for most intensity levels were lessthan 30%. The lower mean absolute error (MAE) and RMSE were found for the lower intensity levels of TCs. In the test results of the typhoon HALONG in 2014, scheme 1 tended to overestimate the intensity by about 20 kts at the early development stage. Scheme 2 slightly reduced the error, resulting in an overestimation by about 5 kts. The MTL models reduced the computational cost about 300% when compared to the single-tasking model, which suggested the feasibility of the rapid production of TC intensity forecasts.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1615-1633
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• 2023

Sargassum horneri is one of the floating algae in the sea, which breeds in large quantities in the Yellow Sea and East China Sea and then flows into the coast of Republic of Korea, causing various problems such as destroying the environment and damaging fish farms. In order to effectively prevent damage and preserve the coastal environment, the development of Sargassum horneri detection algorithms using satellite-based remote sensing technology has been actively developed. However, incorrect detection information causes an increase in the moving distance of ships collecting Sargassum horneri and confusion in the response of related local governments or institutions,so it is very important to minimize false detections when producing Sargassum horneri spatial information. This study applied technology to automatically remove false detection results using the GOCI-II-based Sargassum horneri detection algorithm of the National Ocean Satellite Center (NOSC) of the Korea Hydrographic and Oceanography Agency (KHOA). Based on the results of analyzing the causes of major false detection results, it includes a process of removing linear and sporadic false detections and green algae that occurs in large quantities along the coast of China in spring and summer by considering them as false detections. The technology to automatically remove false detection was applied to the dates when Sargassum horneri occurred from February 24 to June 25, 2022. Visual assessment results were generated using mid-resolution satellite images, qualitative and quantitative evaluations were performed. Linear false detection results were completely removed, and most of the sporadic and green algae false detection results that affected the distribution were removed. Even after the automatic false detection removal process, it was possible to confirm the distribution area of Sargassum horneri compared to the visual assessment results, and the accuracy and precision calculated using the binary classification model averaged 97.73% and 95.4%, respectively. Recall value was very low at 29.03%, which is presumed to be due to the effect of Sargassum horneri movement due to the observation time discrepancy between GOCI-II and mid-resolution satellite images, differences in spatial resolution, location deviation by orthocorrection, and cloud masking. The results of this study's removal of false detections of Sargassum horneri can determine the spatial distribution status in near real-time, but there are limitations in accurately estimating biomass. Therefore, continuous research on upgrading the Sargassum horneri monitoring system must be conducted to use it as data for establishing future Sargassum horneri response plans.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.387-397
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• 2023

Plant height is a growth parameter that provides visible insights into the plant's growth status and has a high correlation with yield, so it is widely used in crop breeding and cultivation research. Investigation of the growth characteristics of crops such as plant height has generally been conducted directly by humans using a ruler, but with the recent development of sensing and image analysis technology, research is being attempted to digitally convert growth measurement technology to efficiently investigate crop growth. In this study, the canopy height of rice grown at various nitrogen fertilization levels was measured using a laser scanner capable of precise measurement over a wide range, and a comparative analysis was performed with the actual plant height. As a result of comparing the point cloud data collected with a laser scanner and the actual plant height, it was confirmed that the estimated plant height measured based on the average height of the top 1% points showed the highest correlation with the actual plant height (R² = 0.93, RMSE = 2.73). Based on this, a linear regression equation was derived and used to convert the canopy height measured with a laser scanner to the actual plant height. The rice growth curve drawn by combining the actual and estimated plant height collected by various nitrogen fertilization conditions and growth period shows that the laser scanner-based canopy height measurement technology can be effectively utilized for assessing the plant height and growth of rice. In the future, 3D images derived from laser scanners are expected to be applicable to crop biomass estimation, plant shape analysis, etc., and can be used as a technology for digital conversion of conventional crop growth assessment methods.