• Journal of Internet of Things and Convergence
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• v.8 no.5
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• pp.119-125
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• 2022

With the recent increase in dementia patients due to aging, measures to prevent their wandering behavior and disappearance are urgently needed. To solve this problem, various authentication methods and location detection techniques have been introduced, but the security problem of personal authentication and a system that can check indoor and outdoor overall was lacking. In order to solve this problem, various authentication methods and location detection techniques have been introduced, but it was difficult to find a system that can check the security problem of personal authentication and indoor/outdoor overall. In this study, we intend to propose a system that can identify personal authentication, basic health status, and overall location indoors and outdoors by using wristband-type wearable devices in a private blockchain environment. In this system, personal authentication uses ECG, which is difficult to forge and highly personally identifiable, Bluetooth beacon that is easy to use with low power, non-contact and automatic transmission and reception indoors, and DGPS that corrects the pseudorange error of GPS satellites outdoors. It is intended to detect wandering behavior and abnormal signs by locating the patient. Through this, it is intended to contribute to the prompt response and prevention of disappearance in case of wandering behavior and abnormal symptoms of dementia patients living at home or in nursing homes.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.967-977
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• 2022

The occurrence and intensity of wildfires are increasing with climate change. Emissions from forest fire smoke are recognized as one of the major causes affecting air quality and the greenhouse effect. The use of satellite product and machine learning is essential for detection of forest fire smoke. Until now, research on forest fire smoke detection has had difficulties due to difficulties in cloud identification and vague standards of boundaries. The purpose of this study is to detect forest fire smoke using Level 1 and Level 2 data of Geostationary Environment Monitoring Spectrometer (GEMS), a Korean environmental satellite sensor, and machine learning. In March 2022, the forest fire in Gangwon-do was selected as a case. Smoke pixel classification modeling was performed by producing wildfire smoke label images and inputting GEMS Level 1 and Level 2 data to the random forest model. In the trained model, the importance of input variables is Aerosol Optical Depth (AOD), 380 nm and 340 nm radiance difference, Ultra-Violet Aerosol Index (UVAI), Visible Aerosol Index (VisAI), Single Scattering Albedo (SSA), formaldehyde (HCHO), nitrogen dioxide (NO₂), 380 nm radiance, and 340 nm radiance were shown in that order. In addition, in the estimation of the forest fire smoke probability (0 ≤ p ≤ 1) for 2,704 pixels, Mean Bias Error (MBE) is -0.002, Mean Absolute Error (MAE) is 0.026, Root Mean Square Error (RMSE) is 0.087, and Correlation Coefficient (CC) showed an accuracy of 0.981.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.57-64
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• 2022

If the inside of a building collapses due to a disaster such as fire, collapse, or natural disaster, the physical security inside the building is likely to become ineffective. Here, physical security is needed to minimize the human casualties and physical damages in the collapsed building. Therefore, this paper proposes an algorithm to minimize the damage in a disaster situation by fusing existing research that detects obstacles and collapsed areas in the building and a deep learning-based object detection algorithm that minimizes human casualties. The existing research uses a single camera to determine whether the corridor environment in which the robot is currently located has collapsed and detects obstacles that interfere with the search and rescue operation. Here, objects inside the collapsed building have irregular shapes due to the debris or collapse of the building, and they are classified and detected as obstacles. We also propose a method to detect rescue requesters-the most important resource in the disaster situation-and minimize human casualties. To this end, we collected open-source disaster images and image data of disaster situations and calculated the accuracy of detecting rescue requesters in disaster situations through various deep learning-based object detection algorithms. In this study, as a result of analyzing the algorithms that detect rescue requesters in disaster situations, we have found that the YOLOv4 algorithm has an accuracy of 0.94, proving that it is most suitable for use in actual disaster situations. This paper will be helpful for performing efficient search and rescue in disaster situations and achieving a high level of physical security, even in collapsed buildings.

• Journal of Convergence for Information Technology
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• v.11 no.8
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• pp.92-99
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• 2021

Light-scattering fine particulate matter monitors can measure particulate matter (PM) concentrations in every second and can be designed in a portable size. They can measure the concentrations of various PM sizes (PM_1.0, PM_2.5, PM_4.0 and PM₁₀) with a single sensor. They measure the number and size of particulate matters and convert them to weight per volume (concentration). These devices show a large error for asian dust. This paper proposes a scheme that compensates the PM_2.5 concenstration error for asian dust by multiple linear regression machine learning in light-scattering PM monitors. This scheme can be effective with only two or three types of PM sizes. The experimental results compare a beta-ray PM monitor of national institute of environmental research and a light-scattering PM monitor during a month. The correlation coefficient (R²) of theses two devices was 0.927 without asian dust, but it was 0.763 due to asian dust during the entire experimental period and improved to 0.944 by the proposed machine learning.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.5
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• pp.179-186
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• 2021

Manipulation of complex objects with an anthropomorphic robot hand like a human hand is a challenge in the human-centric environment. In order to train the anthropomorphic robot hand which has a high degree of freedom (DoF), human demonstration augmented deep reinforcement learning policy optimization methods have been proposed. In this work, we first demonstrate augmentation of human demonstration in deep reinforcement learning (DRL) is effective for object manipulation by comparing the performance of the augmentation-free Natural Policy Gradient (NPG) and Demonstration Augmented NPG (DA-NPG). Then three DRL policy optimization methods, namely NPG, Trust Region Policy Optimization (TRPO), and Proximal Policy Optimization (PPO), have been evaluated with DA (i.e., DA-NPG, DA-TRPO, and DA-PPO) and without DA by manipulating six objects such as apple, banana, bottle, light bulb, camera, and hammer. The results show that DA-NPG achieved the average success rate of 99.33% whereas NPG only achieved 60%. In addition, DA-NPG succeeded grasping all six objects while DA-TRPO and DA-PPO failed to grasp some objects and showed unstable performances.

• Journal of Digital Convergence
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• v.19 no.5
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• pp.93-103
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• 2021

The special means of transportation introduced to improve the mobility of the transportation vulnerable met the number of legal standards in 2016, but lack of development in terms of quality, such as the existence of long waiting times. In order to streamline the operation of special means of transportation, long-term standby traffic, which is the top 25% of the wait time, was extracted from the Daegu Metropolitan Government's special transportation history data, and spatial autocorrelation analysis and social network analysis were conducted. As a result of the analysis, the correlation between the average waiting time of special transportation users and the space was high. As a result of the analysis of internal degree centrality, the peak time zone is mainly visited by general hospitals, while the off-peak time zone shows high long-term waiting demand for visits by lawmakers. The analysis of external degree centrality showed that residential-based traffic demand was high in both peak and off-peak hours. The results of this study are considered to contribute to the improvement of the quality of the operation of special transportation means, and the academic implications and limitations of the study are also presented.

• The Journal of the Acoustical Society of Korea
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• v.40 no.3
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• pp.201-212
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• 2021

The present study was conducted as a pilot investigation to suggest the standards of acoustic performance for classrooms suitable for incomplete hearing people such as children under 9 years of age. Subjective evaluations such as questionnaire and speech intelligibility test were conducted to 264 students at two elementary schools in Cheong-ju in order to analyze the characteristics of the listening environment in the classrooms of the lower grades in elementary school. The survey was undertaken with a total of 264 students at two elementary schools in Cheong-ju, and investigated their satisfaction with the classroom listening environment. As a result, students responded that the most helpful information type for understanding class content is the voice of teacher. In addition, the volume of the current teacher's voice is normal, and the level of clarity is highly satisfactory. As for the acoustic performance of the classroom, the opinion that the noise was normal and the reverberation was very short was found to be dominant in overall satisfaction with the listening environment. Meanwhile, as a result of speech intelligibility test using the word list selected for the lower grade students of elementary school, it could be inferred that the longitudinal axis distance from the sound source in the case of 8-year-olds is a factor that affects speech recognition.

• Ecology and Resilient Infrastructure
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• v.9 no.4
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• pp.237-246
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• 2022

Owing to their high bioproductivity and unique physical environment, estuarine wetlands are gaining importance in national biodiversity management and habitat conservation. With regard to conservation and management of estuarine wetlands, this study analyzed the ecological characteristics of Gahwacheon Estuarine Wetland, an open estuary with various habitat types. Data from vegetation and biotic surveys have shown that 12 plant communities of five physiognomic vegetation types, including lentic herbaceous vegetation, halophytic herbaceous vegetation, and chasmophytic herbaceous vegetation. Due to the discharge of Namgang Dam and the effect of the tide, vegetation are distributed along the narrow waterside area. In terms of biodiversity, a total of 715 species, including 12 endangered wildlife species, were identified. Species diversity was relatively high in sections I and III where various riverbed structures and microhabitats were distributed. Due to the effect of the brackish water area following the inflow of seawater, endangered wildlife of various functional groups were also found to be distributed, indicating the high conservation value of that area. The collection of ecological information of the Gahwacheon Estuarine Wetland can be used as a framework for establishing the basis for conservation and management of the estuarine ecosystem and support policy establishment.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1249-1258
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• 2022

Marine debris is defined as a substance that is intentionally or inadvertently left on the shore or is introduced or discharged into the ocean, which has or is likely to have a harmful effect on the marine environments. In this study, the detection of marine debris and the analysis of the amount of change on marine debris were performed using the object detection method for an efficient method of identifying the quantity of marine debris and analyzing the amount of change. The study area is Yuho Mongdol Beach in the northeastern part of Geoje Island, and the amount of change was analyzed through images collected at 15-minute intervals for 32 days from September 12 to October 14, 2022. Marine debris detection using YOLOv5x, a one-stage object detection model, derived the performance of plastic bottles mAP 0.869 and styrofoam buoys mAP 0.862. As a result, marine debris showed a large decrease at 8-day intervals, and it was found that the quantity of Styrofoam buoys was about three times larger and the range of change was also larger.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.6
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• pp.869-877
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• 2022

This study aims to analyze the factors affecting the re-subscription period upon initial termination of the reverse mortgage subscription. The study utilized the Korea Housing Finance Corporation's database to extract the information regarding re-subscribers of the reverse mortgage from July 2007 to June 2021. The ordered logit model was employed and found that a set of user (subscriber) characteristics are influential towards the re-subscription period. Among the individual characteristics, changes in age group, marital status from married to single-living, maintaining single-living, and the initial subscription period were found statistically significant, highlighting that the increase in the initial subscription period decreased the re-subscription period. Among the housing (home equity) characteristics, changes in housing price and ownership type (single and partial ownership) were statistically significant, indicating that the change in ownership type decreases the re-subscription period. Lastly, the variables related to loan terms were found significant, revealing that changes in payout method and schedule were both increasing factors of the re-subscription period. Based on the findings, necessary policy implications can be considered to secure the returning subscribers of the reverse mortgage effectively.