Journal of the Korea Institute of Information and Communication Engineering
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v.26
no.2
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pp.294-305
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2022
Recently, drones have been widely used to solve environmental problems such as environmental protection and natural disaster monitoring. This study focuses on the problem of the search altitude of drones when using drones to search for air pollution in order to maintain the urban air environment. In particular, when exploring air pollution in cities using drones, various experiments are conducted to determine the appropriate search altitude for each air pollution source and each communication module. Through the experiment, the maximum measurable altitude for the most common air pollutants, such as CO (carbon monoxide), NO2 (nitrogen dioxide), O3 (ozone), and P10, P2.5 (fine dust), was identified, and the effective search altitude for each air pollution source was determined. As a result of the experiment, three types of drone search altitudes including legally measurable altitudes were suggested. The communication module measurable altitude was 60m to 120m depending on the communication module, and the effective measurable altitude was analyzed from 10m to 100m.
Venkatesh Sivaprakasam;Vartika Kulshrestha;Godlin Atlas Lawrence Livingston;Senthilnathan Arumugam
KSII Transactions on Internet and Information Systems (TIIS)
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v.17
no.7
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pp.1873-1893
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2023
The development of lightweight, low energy and small-sized sensors incorporated with the wireless networks has brought about a phenomenal growth of Wireless Sensor Networks (WSNs) in its different fields of applications. Moreover, the routing of data is crucial in a wide number of critical applications that includes ecosystem monitoring, military and disaster management. However, the time-delay, energy imbalance and minimized network lifetime are considered as the key problems faced during the process of data transmission. Furthermore, only when the functionality of cluster head selection is available in WSNs, it is possible to improve energy and network lifetime. Besides that, the task of cluster head selection is regarded as an NP-hard optimization problem that can be effectively modelled using hybrid metaheuristic approaches. Due to this reason, an Improved Coyote Optimization Algorithm-based Clustering Technique (ICOACT) is proposed for extending the lifetime for making efficient choices for cluster heads while maintaining a consistent balance between exploitation and exploration. The issue of premature convergence and its tendency of being trapped into the local optima in the Improved Coyote Optimization Algorithm (ICOA) through the selection of center solution is used for replacing the best solution in the search space during the clustering functionality. The simulation results of the proposed ICOACT confirmed its efficiency by increasing the number of alive nodes, the total number of clusters formed with the least amount of end-to-end delay and mean packet loss rate.
After the hydrogen fluoride leak in Gumi in 2012, the government has been systemizing the disaster management system, such as responding to and managing chemical accidents. In particular, the Ministry of the Interior and Safety (MOIS) is in charge of evacuation of residents following chemical accidents based on the Framework Act on Management of Disaster and Safety. In this study, an application plan was presented to support and utilize the decision-making support for evacuation of residents after a chemical accident using the chemical accident investigation equipment of the National Disaster Management Research Institute (NDMI). In the equipment operation system for scientific information collection due to chemical accidents, the roles and purpose of use of long/short distance measurement equipment were presented according to regular and emergency situations. Using the data acquired through long/short distance measurement equipment, it can be used as basic data for resident evacuation decision-making by monitoring whether chemicals are detected in an emergency and managing data on detected substances by company in a regular situation. As a result of measuring chemical substances in order to verify on-site usability by equipment only for the regular operation system, it was confirmed that real-time detection of chemical substances is possible with long distance measuring equipment. In addition, it was confirmed that it was necessary to check the measurable distance and range of the equipment in the future. In the case of short distance measurement equipment, hydrocarbon-based substances were mainly detected, and it was confirmed that it was measured at a higher level in Ulsan-Mipo National Industrial Complex than in Onsan National Industrial Complex. It is expected that it can be used as basic data to support decision-making in the event of chemical accidents through continuous data construction in the future.
The increasing atmospheric imbalance caused by climate change leads to an elevation in precipitation, resulting in a heightened frequency of flooding. Consequently, there is a growing need for technology to detect and monitor these occurrences, especially as the frequency of flooding events rises. To minimize flood damage, continuous monitoring is essential, and flood areas can be detected by the Synthetic Aperture Radar (SAR) imagery, which is not affected by climate conditions. The observed data undergoes a preprocessing step, utilizing a median filter to reduce noise. Classification techniques were employed to classify water bodies and non-water bodies, with the aim of evaluating the effectiveness of each method in flood detection. In this study, the Otsu method and Support Vector Machine (SVM) technique were utilized for the classification of water bodies and non-water bodies. The overall performance of the models was assessed using a Confusion Matrix. The suitability of flood detection was evaluated by comparing the Otsu method, an optimal threshold-based classifier, with SVM, a machine learning technique that minimizes misclassifications through training. The Otsu method demonstrated suitability in delineating boundaries between water and non-water bodies but exhibited a higher rate of misclassifications due to the influence of mixed substances. Conversely, the use of SVM resulted in a lower false positive rate and proved less sensitive to mixed substances. Consequently, SVM exhibited higher accuracy under conditions excluding flooding. While the Otsu method showed slightly higher accuracy in flood conditions compared to SVM, the difference in accuracy was less than 5% (Otsu: 0.93, SVM: 0.90). However, in pre-flooding and post-flooding conditions, the accuracy difference was more than 15%, indicating that SVM is more suitable for water body and flood detection (Otsu: 0.77, SVM: 0.92). Based on the findings of this study, it is anticipated that more accurate detection of water bodies and floods could contribute to minimizing flood-related damages and losses.
Estimating damage in each sector that can be caused by volcanic ash deposition, is very important to prepare the volcanic ash disaster. In this study, we showed predicted-Korean-volcanic-ash damage of each sector by using volcanic ash diffusion simulation and spatial-data-based volcanic ash damage sector in previous study. To this end, volcanic ash related base maps were generated by collecting and processing spatial information data. Finally, we showed Korean-volcanic-ash-deposition damages by sector using the collected Mt. Aso volcanic ash scenarios via overlapping analysis. As a result, volcanic-ash-related damages were expected to occur in the 162 and 134 districts for each Aso volcanic ash scenarios, since those districts exceeds the minimum volcanic ash damage criterion of 0.01 mm. Finally, we compared possible volcanic ash damages by sectors using collected and processed spatial data, after selecting administrative districts(Scenario 190805- Kangwon-do, Kyungsangbuk-do; Scenario 190811-Chuncheon-si, Hongcheon-si) with the largest amount of volcanic ash deposition.
Park, Jae Hoon;Lee, Ho;Kim, Chang Yong;Park, Chi Myeon;Kim, Ji Eun
Journal of Korean Tunnelling and Underground Space Association
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v.23
no.6
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pp.559-575
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2021
Due to the increase in ground excavation work, the possibility of ground subsidence accidents is increasing. And it is very difficult to prevent these risk fundamentally through institutional reinforcement such as the special law for underground safety management. As for the various cases of urban ground excavation practice, the ground subsidence behavior characteristics which is predicted using various information before excavation showed a considerable difference that could not be ignored compared to the results real construction data. Changes in site conditions such as seasonal differences in design and construction period, changes in construction methods depending on the site conditions and long-term construction suspension due to various reasons could be considered as the main causes. As the countermeasures, the safety management system through various construction information is introduced, but there is still no suitable system which can predict the effect of excavation and risk assessment. In this study, a web-based system was developed in order to predict the degree of impact on the ground subsidence and surrounding structures in advance before ground excavation and evaluate the risk in the design and construction of urban ground excavation projects. A system was built using time series analysis technique that can predict the current and future behavior characteristics such as ground water level and settlement based on past field construction records with field monitoring data. It was presented as a geotechnical data visualization (GDV) technology for risk reduction and disaster management based on web-based system, Using this newly developed web-based assessment system, it is possible to predict ground excavation impact prediction and risk assessment.
Keunchang, Jang;Sunghyun, Min;Inhye, Kim;Junghwa, Chun;Myoungsoo, Won
Korean Journal of Agricultural and Forest Meteorology
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v.24
no.4
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pp.346-352
/
2022
Mountain meteorology in South Korea that is covered mountains with complex terrain is important for understanding and managing the forest disaster and forest ecosystems. In particular, recent changes in dryness and/or rainfall intensity due to climate change may cause an increase in the possibility of forest disasters. Therefore, accurate monitoring of mountain meteorology is needed for efficient forest management. Korea Forest Service (KFS) is establishing the Automatic Mountain Meteorology Observation Stations (AMOS) in the mountain regions since 2012. 464 AMOSs are observing various meteorological variables such as air temperature, relative humidity, wind speed and direction, precipitation, soil temperature, and air pressure for every minute, which is conducted the quality control (QC) to retain data reliability. QC process includes the physical limit test, step test, internal consistency test, persistence test, climate range test, and median filter test. All of AMOS observations are open to use, which can be found from the Korean Mountain Meteorology Information System (KoMIS, http://mtweather.nifos.go.kr/) of the National Institute of Forest Science and the Public Data Portal (https://public.go.kr/). AMOS observations with guaranteed quality can be used in various forest fields including the public safety, forest recreation, forest leisure activities, etc., and can contribute to the advancement of forest science and technology. In this paper, a series of processes are introduced to collect and use the AMOS dataset in the mountain region in South Korea.
KSCE Journal of Civil and Environmental Engineering Research
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v.43
no.5
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pp.667-674
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2023
In the event of a disaster occurring within a building, the prompt and efficient evacuation and rescue of occupants within the building becomes the foremost priority to minimize casualties. For the purpose of such rescue operations, it is essential to ascertain the distribution of individuals within the building. Nevertheless, there is a primary dependence on accounts provided by pertinent individuals like building proprietors or security staff, alongside fundamental data encompassing floor dimensions and maximum capacity. Consequently, accurate determination of the number of occupants within the building holds paramount significance in reducing uncertainties at the site and facilitating effective rescue activities during the golden hour. This research introduces a methodology employing computer vision algorithms to count the number of occupants within distinct building locations based on images captured by installed multiple CCTV cameras. The counting methodology consists of three stages: (1) establishing virtual Lines of Interest (LOI) for each camera to construct a multi-camera network environment, (2) detecting and tracking people within the monitoring area using deep learning, and (3) aggregating counts across the multi-camera network. The proposed methodology was validated through experiments conducted in a five-story building with the average accurary of 89.9% and the average MAE of 0.178 and RMSE of 0.339, and the advantages of using multiple cameras for occupant counting were explained. This paper showed the potential of the proposed methodology for more effective and timely disaster management through common surveillance systems by providing prompt occupancy information.
Wireless sensor-actuator networks (WSANs) enhance the existing wireless sensor networks (WSNs) by equipping sensor nodes with an actuator. The actuators work with the sensor nodes and perform application-specific operations. The WSAN systems have several applications such as disaster relief, intelligent building, military surveillance, health monitoring, and infrastructure security. These applications require capability of reliable data transfer to act responsively and accurately. Biologically inspired modeling techniques have received considerable attention for achieving robustness, scalability, and adaptability, while retaining individual simplicity. In this paper, an epidemic-inspired algorithm for data dissemination with delay constraints while minimizing energy consumption in WSAN is proposed. The steady states and system stability are analyzed using control theory. Also, simulation results indicate that the proposed scheme provides desirable dissemination delay and energy saving.
Journal of the Korean Society of Environmental Restoration Technology
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v.14
no.1
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pp.105-119
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2011
In order to develop the indicators which evaluate the management effectiveness for the protected forest areas in Korea, candidate indicators were listed based on literature and experts interviews, then questionnaire survey on the experts were conducted. 5 elements of context, planning, input, process, output and outcome and 32 indicators were selected. Context element includes 6 indicators of 1) documentation and assessment of values; 2) documentation and assessment of threats, 3) influence of government policy, 4) related regulations, 5) community cooperation and 6) the structure of management organization. 6 indicators of Planning element were 1) the management objective, 2) protected area design, 3) protected area size and number, 4) representation, 5) standards and categories and 6) management planning. Input element of 3 indicators were 1) management staff, 2) funding, 3) establishment and application of information. Process element were consisted of 1) governance, 2) management guidelines, 3) human resource management, 4) law enforcement, 5) eco-management, 6) disaster management, 7) education program and 8) research and monitoring. The element of outputs and outcomes were 1) accomplishment of plan, 2) accomplishment of program, 3) private land management, 4) threats change, 5) biodiversity change, 6) ecosystem health and vitality, 7) impact on community, 8) international management level and 9) visitors' satisfaction and variation in civil compliant. It is recommended to have further research on evaluation methods development by applying those above developed indicators for the protected forest areas to ensure the practicality of the indicators.
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