• Korean Journal of Remote Sensing
• /
• v.18 no.1
• /
• pp.61-69
• /
• 2002

The purpose of Electro-Optical Camera (EOC), the primary payload of KOMPSAT-1, is to collect high resolution visible imagery of the Earth including Korean Peninsula. EOC images will be distributed to the public or many user groups including government, public corporations, academic or research institutes. KARI will offer the online service to the users through internet. Some application, e.g., generation of Digital Elevation Model (DEM), needs a secondary data such as satellite ephemeris data, attitude data to process the EOC imagery. EOC imagery with these ancillary information will be distributed in a file of Hierarchical Data Format (HDF) file formal. HDF is a physical file format that allows storage of many different types of scientific data including images, multidimensional data arrays, record oriented data, and point data. By the lack of public domain softwares supporting HDF file format, many public users may not access EOC data without difficulty. The purpose of this research is to develop a browsing system of EOC data for the general users not only for scientists who are the main users of HDF. The system is PC-based and huts user-friendly interface.

• International Journal of Computer Science & Network Security
• /
• v.22 no.5
• /
• pp.73-78
• /
• 2022

The aim of this paper is to investigate the prevalence of Deep Learning in the literature on Fire & Rescue Service. It is found that deep learning techniques are only beginning to benefit the firefighters. The popular areas where deep learning techniques are making an impact are situational awareness, decision making, mental stress, injuries, well-being of the firefighter such as his sudden fall, inability to move and breathlessness, path planning by the firefighters while getting to an fire scene, wayfinding, tracking firefighters, firefighter physical fitness, employment, prediction of firefighter intervention, firefighter operations such as object recognition in smoky areas, firefighter efficacy, smart firefighting using edge computing, firefighting in teams, and firefighter clothing and safety. The techniques that were found applied in firefighting were Deep learning, Traditional K-Means clustering with engineered time and frequency domain features, Convolutional autoencoders, Long Short-Term Memory (LSTM), Deep Neural Networks, Simulation, VR, ANN, Deep Q Learning, Deep learning based on conditional generative adversarial networks, Decision Trees, Kalman Filters, Computational models, Partial Least Squares, Logistic Regression, Random Forest, Edge computing, C5 Decision Tree, Restricted Boltzmann Machine, Reinforcement Learning, and Recurrent LSTM. The literature review is centered on Firefighters/firemen not involved in wildland fires. The focus was also not on the fire itself. It must also be noted that several deep learning techniques such as CNN were mostly used in fire behavior, fire imaging and identification as well. Those papers that deal with fire behavior were also not part of this literature review.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.12
• /
• pp.4476-4491
• /
• 2021

Video service providers tend to face user network problems in the process of transmitting video streams. They strive to provide user with superior video quality in a limited bitrate environment. It is necessary to accurately determine the target bitrate range of the video under different quality requirements. Recently, several schemes have been proposed to meet this requirement. However, they do not take the impact of visual influence into account. In this paper, we propose a new multi-category model to accurately predict the target bitrate range with target visual quality by machine learning. Firstly, a dataset is constructed to generate multi-category models by machine learning. The quality score ladders and the corresponding bitrate-interval categories are defined in the dataset. Secondly, several types of spatial-temporal features related to VMAF evaluation metrics and visual factors are extracted and processed statistically for classification. Finally, bitrate prediction models trained on the dataset by RandomForest classifier can be used to accurately predict the target bitrate of the input videos with target video quality. The classification prediction accuracy of the model reaches 0.705 and the encoded video which is compressed by the bitrate predicted by the model can achieve the target perceptual quality.

• Korean Journal of Remote Sensing
• /
• v.38 no.4
• /
• pp.421-434
• /
• 2022

Fine particulate matter (PM2.5) has been the biggest environmental problem in Korea since the 2010s. The present study considers the value of urban forests and green infrastructure as an ecosystem service (ES) concept for PM2.5 reduction based on satellite and spatial data, with a focus on Seoul, Korea A method for the spatial ES assessment that considers social demand variables such as population and land price is suggested. First, an ES assessment based on natural environment information confirms that, while the vitality of vegetation is relatively low, the ES is high in the city center and residential areas, where the concentration of PM2.5 is high. Then, the ES assessment considering social demand (i.e., the ESS) confirms the existence of higher PM2.5 values in residential areas with high population density, and in main downtown areas. This is because the ESS of urban green infrastructure is high in areas with high land prices, high population density, and above-average PM2.5 concentrations. Further, when a future green infrastructure improvement scenario that considers the urban forest management plan is applied, the area of very high ESS is increased by 74% when the vegetation greenness of the green infrastructure in the residential area is increased by only 20%. This result suggests that green infrastructure and urban forests in the residential area should be continuously expanded and managed in order to maximize the PM2.5 reduction ES.

• Journal of the Society of Disaster Information
• /
• v.19 no.4
• /
• pp.924-935
• /
• 2023

Purpose: The central aim of this study is to leverage machine learning techniques for the classification of Intrusion Detection System (IDS) data, with a specific focus on identifying the variables responsible for enhancing overall performance. Method: First, we classified 'R2L(Remote to Local)' and 'U2R (User to Root)' attacks in the NSL-KDD dataset, which are difficult to detect due to class imbalance, using seven machine learning models, including Logistic Regression (LR) and K-Nearest Neighbor (KNN). Next, we use the SHapley Additive exPlanation (SHAP) for two classification models that showed high performance, Random Forest (RF) and Light Gradient-Boosting Machine (LGBM), to check the importance of variables that affect classification for each model. Result: In the case of RF, the 'service' variable and in the case of LGBM, the 'dst_host_srv_count' variable were confirmed to be the most important variables. These pivotal variables serve as key factors capable of enhancing performance in the context of classification for each respective model. Conclusion: In conclusion, this paper successfully identifies the optimal models, RF and LGBM, for classifying 'R2L' and 'U2R' attacks, while elucidating the crucial variables associated with each selected model.

• International Journal of Computer Science & Network Security
• /
• v.24 no.4
• /
• pp.179-191
• /
• 2024

With the advancement of modern technology, cyber-attacks are always rising. Specialized defense systems are needed to protect organizations against these threats. Malicious behavior in the network is discovered using security tools like intrusion detection systems (IDS), firewall, antimalware systems, security information and event management (SIEM). It aids in defending businesses from attacks. Delivering advance threat feeds for precise attack detection in intrusion detection systems is the role of cyber-threat intelligence (CTI) in the study is being presented. In this proposed work CTI feeds are utilized in the detection of assaults accurately in intrusion detection system. The ultimate objective is to identify the attacker behind the attack. Several data sets had been analyzed for attack detection. With the proposed study the ability to identify network attacks has improved by using machine learning algorithms. The proposed model provides 98% accuracy, 97% precision, and 96% recall respectively.

• Korean Journal of Plant Taxonomy
• /
• v.37 no.2
• /
• pp.155-196
• /
• 2007

This study was carried out to elucidate floristic data of Chirisan National Park by performing practical field investigation for 1 year and literatures survey simultaneously in 2004. The data from field study were yielded based on voucher specimens. Total number and components of flora of Chirisan National Park is revealed as 1,825 taxa. Among 1,825 taxa, 708 taxa were identified in this field study, and these were comprised 37 orders, 109 families, 382 genera, 590 species 3 subspecies 95 varities 20 forms. Unconfirmed taxa in field but recorded previous literatures were 1,117 taxa, which consist of 41 orders 130 families 500 genera 901 species 9 subspecies 140 varities 67 forms. In conclusion, there may be provisionally 1,825 (708+1,117) taxa in Chirisan National Park. The 43 taxa were firstly found out in this field study. In the floristic data from field study, the number of Korean endemic plants were 31 taxa, the rare and endangered plants which was designated by Korea Forest Service were 21 taxa, the taxa that is more than the third degree among the floristic regional indicator plants which was designated by Korean Ministry of Environment were 40 taxa, and naturalized alien plants to Korea were 27 taxa respectively. Among 1,117 taxa which were not found in this study but recorded in 22 previous literatures, the number of Korean endemic plants were 45 taxa, the rare and endangered plants which was designated by Korea Forest Service were 45 taxa, the taxa that is more than the third degree among the floristic regional indicator plants which was designated by Ministry of Environment were 121 taxa, and naturalized alien plants to Korea were 31 taxa. It can be inferred that the vegetation of Chirisan National Park was changing by some factors and valuable plant resources were tend to diminishing by such as human interference and developments.

• Korean Journal of Plant Resources
• /
• v.24 no.1
• /
• pp.76-88
• /
• 2011

The flora of vascular plants in the mountains located in the Uiryeong-gun area, in the South province of the Korean Peninsula, such as Jagul-san(897.1 m), 676 Highland(676 m), Byeokhwa-san(522 m), and the Bangeo-san(530.4 m), was investigated between April 2008 and August 2009. These investigations found 580 taxa consisting of 496 species, 1 subspecies, 77 varieties, and 6 forms, found within 319 genera under 103 families. The count totaled at 744 taxa(16.2% of all vascular plant taxa in Korea), which was made up of 648 species, 3 subspecies, 81 varieties, and 12 forms, found within of 362 genera under 109 families, when voucher specimens from the previous research studies were added. Forests of the investigated areas were generally composed of mixed Pinus densiflora and Quercus sp. The areas with comparatively excellent vegetation were the valley neighboring Baekun-sa(temple) (in the eastern slope of Mt. Jagul-san), the southwest slope of Mt. 676 Highland, the eastern slope of Mt. Byeokhwa-san, and the northern slope of Mt. Bangeo-san. 10 families were collected in abundance: Compositae, Graminae, Leguminosae, Liliaceae, Rosaceae, Cyperaceae, Labiatae, Polygonaceae, Ranunculaceae, and Violaceae these families made up 50% of all collected taxa. 19 taxa were endemic to the area, including Salix hallaisanensis H.Lev, S. koriyanagi Kimura, Aconitum austrokoreense Koidz, A. pseudolaeve Nakai, Clematis trichotoma Nakai, Thalictrum uchiyamai Nakai, Stewartia pseudocamellia Maxim, Philadelphus schrenkii Rupr., Lespedeza ${\times}$ robusta Nakai, Vicia chosenensis Ohwi, Euonymus trapococca Nakai, and Angelica cartilagino-marginata var. distans(Nakai) Kitag. Eight of the taxa were rare and endangered plants, as designated by the Korea Forest Service, including Jeffersonia dubia(Maxim.) Baker & S. Moore and Viola diamantiaca Nakai. 38 taxa of alien plants were found. Vegetation of the surveyed areas falls in the South province of the Korean Peninsula. Of all the taxa collected, 463 taxa(10.06% of all vascular plants in Korea) are considered useful plants, 231 taxa are edible, 193 taxa have medicinal uses, 65 taxa are used ornamentally, 234 taxa are important forage, 3 taxa are used as an industrial raw material, 17 taxa are used for timber, 18 taxa contain useful dyes, and 7 taxa are used for fiber.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.21 no.3
• /
• pp.175-186
• /
• 2019

Efforts have been made to introduce the climate smart agriculture (CSA) for adaptation to future climate conditions, which would require collection and management of site specific meteorological data. The objectives of this study were to identify requirements for construction of agricultural meteorology information service system (AMISS) using technologies that lead to the fourth industrial revolution, e.g., internet of things (IoT), artificial intelligence, and cloud computing. The IoT sensors that require low cost and low operating current would be useful to organize wireless sensor network (WSN) for collection and analysis of weather measurement data, which would help assessment of productivity for an agricultural ecosystem. It would be recommended to extend the spatial extent of the WSN to a rural community, which would benefit a greater number of farms. It is preferred to create the big data for agricultural meteorology in order to produce and evaluate the site specific data in rural areas. The digital climate map can be improved using artificial intelligence such as deep neural networks. Furthermore, cloud computing and fog computing would help reduce costs and enhance the user experience of the AMISS. In addition, it would be advantageous to combine environmental data and farm management data, e.g., price data for the produce of interest. It would also be needed to develop a mobile application whose user interface could meet the needs of stakeholders. These fourth industrial revolution technologies would facilitate the development of the AMISS and wide application of the CSA.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.49 no.4
• /
• pp.15-29
• /
• 2021

The study aims to grasp the perception and appraisal of urban park users through text analysis. This study used Google review data provided by Google Maps. Google Maps Review is an online review platform that provides information evaluating locations through social media and provides an understanding of locations from the perspective of general reviewers and regional guides who are registered as members of Google Maps. The study determined if the Google Maps Reviews were useful for extracting meaningful information about the user perceptions and appraisals for parks management plans. The study chose three urban parks in Seoul, South Korea; Seoul Forest, Boramae Park, and Olympic Park. Review data for each of these three parks were collected via web crawling using Python. Through text analysis, the keywords and network structure characteristics for each park were analyzed. The text was analyzed, as were park ratings, and the analysis compared the reviews of residents and foreign tourists. The common keywords found in the review comments for the three parks were "walking", "bicycle", "rest" and "picnic" for activities, "family", "child" and "dogs" for accompanying types, and "playground" and "walking trail" for park facilities. Looking at the characteristics of each park, Seoul Forest shows many outdoor activities based on nature, while the lack of parking spaces and congestion on weekends negatively impacted users. Boramae Park has the appearance of a city park, with various facilities providing numerous activities, but reviewers often cited the park's complexity and the negative aspects in terms of dog walking groups. At Olympic Park, large-scale complex facilities and cultural events were frequently mentioned, emphasizing its entertainment functions. Google Maps Review can function as useful data to identify parks' overall users' experiences and general feelings. Compared to data from other social media sites, Google Maps Review's data provides ratings and understanding factors, including user satisfaction and dissatisfaction.