• Proceedings of the Korea Information Processing Society Conference
• /
• 2021.11a
• /
• pp.1144-1147
• /
• 2021

코로나19 대유행으로 인해 전 세계가 원격으로 일상을 옮겨가면서 인터넷 트래픽량이 증가하고 보안 위협 또한 높아졌다. 높은 보안성이 요구되는 현 상황에 대응하기 위해 본 논문에서는 Suricata와 Elastic Stack, Kafka를 이용해 보안관제 로그 분석시스템을 구축하였다. 실시간으로 공격을 탐지하고 로그를 수집해 유의미한 데이터를 도출하여 시각화한다. 또한 시각화 한 대시보드를 제공함으로써 사용자는 공격의 위험도를 파악할 수 있고 앞으로의 공격을 대비할 수 있다.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2022.05a
• /
• pp.297-297
• /
• 2022

홍수 발생으로 인해 야기되는 피해가 매년 일어나고 있으며, 현재 수재와 관련된 방대한 데이터가 축적되어 있어 이를 활용한 연구들이 진행되고 있다. 데이터를 기반으로 홍수 전후의 시공간적인 변화에 대한 다양한 분석이 가능하여 수재 대응에 유용하게 활용될 수 있다. 본 연구에서는 원격 탐지 및 재분석 데이터를 활용하여 파머 가뭄 지수(PDSI), 강우량, 유출량, 실제 증발산량(AET), 대기 온도 등의 수재와 관련된 요인들에 대한 지수분석을 통해 공간 변화를 파악하고 경향을 분석하였다. 이를 통해 자연 현상을 다루는 환경 영역에서의 데이터 기반 연구의 가능성이 확대될 수 있으며, 향후 연구에 활용하고자 한다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2008.05a
• /
• pp.789-792
• /
• 2008

범죄 예방과 자원 보호를 위해 CCTV 카메라를 이용하는 방범 시스템의 필요성은 점차 커지고 있다. 아날로그 형식에서부터 디지털 형식으로 발전된 형태의 방범 시스템이 개발되고 사용 중이지만, 비용이 높고 효율성이 떨어지는 문제가 있다. 본 논문에서는 웹 서비스 기반의 서버에 적외선 카메라를 연결하고 사용자가 사전 인지 없이도 클라이언트에서 실시간으로 침입을 탐지하여 적절하게 대처할 수 있는 방범 시스템을 설계하고 구현한다.

• Korean Journal of Remote Sensing
• /
• v.39 no.5_4
• /
• pp.1173-1183
• /
• 2023

Over the past 20 years, Korea's overall import and export cargo volume has increased at an average annual rate of approximately 5.3%. About 99% of the cargo is still being transported by sea. Due to recent increases in maritime cargo volume, congestion in maritime logistics has become challenging due to factors such as the COVID-19 pandemic and conflicts. Continuous monitoring of ports has become crucial. Various ground observation systems and Automatic Identification System (AIS) data have been utilized for monitoring ports and conducting numerous preliminary studies for the efficient operation of container terminals and cargo volume prediction. However, small and developing countries' ports face difficulties in monitoring due to environmental issues and aging infrastructure compared to large ports. Recently, with the increasing utility of artificial satellites, preliminary studies have been conducted using satellite imagery for continuous maritime cargo data collection and establishing ocean monitoring systems in vast and hard-to-reach areas. This study aims to visually detect ships docked at berths in the Busan New Port using high-resolution satellite imagery and quantitatively evaluate berth utilization rates. By utilizing high-resolution satellite imagery from Compact Advanced Satellite 500-1 (CAS500-1), Korea Multi-Purpose satellite-3 (KOMPSAT-3), PlanetScope, and Sentinel-2A, ships docked within the port berths were visually detected. The berth utilization rate was calculated using the total number of ships that could be docked at the berths. The results showed variations in berth utilization rates on June 2, 2022, with values of 0.67, 0.7, and 0.59, indicating fluctuations based on the time of satellite image capture. On June 3, 2022, the value remained at 0.7, signifying a consistent berth utilization rate despite changes in ship types. A higher berth utilization rate indicates active operations at the berth. This information can assist in basic planning for new ship operation schedules, as congested berths can lead to longer waiting times for ships in anchorages, potentially resulting in increased freight rates. The duration of operations at berths can vary from several hours to several days. The results of calculating changes in ships at berths based on differences in satellite image capture times, even with a time difference of 4 minutes and 49 seconds, demonstrated variations in ship presence. With short observation intervals and the utilization of high-resolution satellite imagery, continuous monitoring within ports can be achieved. Additionally, utilizing satellite imagery to monitor changes in ships at berths in minute increments could prove useful for small and developing country ports where harbor management is not well-established, offering valuable insights and solutions.

• Korean Journal of Remote Sensing
• /
• v.39 no.5_4
• /
• pp.1135-1144
• /
• 2023

Many agricultural reservoirs in South Korea, constructed before 1970, have become aging facilities. The majority of small-scale reservoirs lack measurement systems to ascertain basic specifications and water levels, classifying them as unmeasured reservoirs. Furthermore, continuous sedimentation within the reservoirs and industrial development-induced water quality deterioration lead to reduced water supply capacity and changes in reservoir morphology. This study utilized Light Detection And Ranging (LiDAR) sensors, which provide elevation information and allow for the characterization of surface features, to construct high-resolution Digital Surface Model (DSM) and Digital Elevation Model (DEM) data of reservoir facilities. Additionally, bathymetric measurements based on multibeam echosounders were conducted to propose an updated approach for determining reservoir capacity. Drone-based LiDAR was employed to generate DSM and DEM data with a spatial resolution of 50 cm, enabling the display of elevations of hydraulic structures, such as embankments, spillways, and intake channels. Furthermore, using drone-based hyperspectral imagery, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) were calculated to detect water bodies and verify differences from existing reservoir boundaries. The constructed high-resolution DEM data were integrated with bathymetric measurements to create underwater contour maps, which were used to generate a Triangulated Irregular Network (TIN). The TIN was utilized to calculate the inundation area and volume of the reservoir, yielding results highly consistent with basic specifications. Considering areas that were not surveyed due to underwater vegetation, it is anticipated that this data will be valuable for future updates of reservoir capacity information.

• Journal of Internet Computing and Services
• /
• v.12 no.5
• /
• pp.47-61
• /
• 2011

Recently, the importance of digital forensic has been magnified because of the dramatic increase of cyber crimes and the increasing complexity of the investigation of target systems such as PCs, servers, and database systems. Moreover, some systems have to be investigated with live forensic techniques. However, even though live forensic techniques have been improved, they are still vulnerable to anti-forensic activities when the target systems are remotely accessible by criminals or their accomplices. To solve this problem, we first suggest a layer-based model and the anti-forensic scenarios which can actually be applicable to each layer. Our suggested model, the Anti-Forensic Activites layer-based model, has 5 layers - the physical layer, network layer, OS layer, database application layer and data layer. Each layer has possible anti-forensic scenarios with detailed commands. Second, we propose a fuzzy expert system for effectively detecting anti-forensic activities. Some anti-forensic activities are hardly distinguished from normal activities. So, we use fuzzy logic for handling ambiguous data. We make rule sets with extracted commands and their arguments from pre-defined scenarios and the fuzzy expert system learns the rule sets. With this system, we can detect anti-forensic activities in real time when performing live forensic.

• Journal of the Korean Society of Fisheries and Ocean Technology
• /
• v.31 no.3
• /
• pp.247-255
• /
• 1995

This paper describes a personal computer(PC) based color fish finder to improve some problem of the commercial one. The commercial fish finder has no function of the echo data logging and replaying. The authors developed two types of the PC based color fish finder. One is a master type composed of a PC, a digital input-output board, and analog to digital converting (A/D) board and an ultrasonic transceiver unit, the other is a slave type composed of a PC and an A/D board. To test the performances of the master type experiments were carried out in air and in a water tank. It is found that the designed master type fish finder displays very well an eight-colored echogram by one dot resolution to the left side of the PC monitor. Also, the depth of echo signal was corresponds very well to the range from the transducer to a target. The sampling interval of echo signal is about 0.1m and the time of A/D conversion is 30 $\mu$sec. On the other hand, to test the performances of the slave type a raw data of echo signals from a data logger was supplied directly or via RF transceivers to the slave type one. From this experiment, it is confirmed the slave type is useful to replay the echo signal from the data logger or a telesounder.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.26 no.6
• /
• pp.175-181
• /
• 2022

The condition of infrastructure deteriorates as the service life increases. Since most infrastructure in South Korea were intensively built during the period of economic growth, the proportion of outdated infrastructure is rapidly increasing now. Aging of such infrastructure can lead to safety accidents and even human casualties. To prevent these issues in advance, periodic and accurate inspection is essential. For this reason, the need for research to detect various types of damage using computer vision and deep learning is increasingly required in the field of remotely controlled or autonomous inspection. To this end, this study proposed a neural network structure that can detect concrete damage by classifying it into three types. In particular, the proposed neural network can detect them more accurately through a hierarchical learning technique. This neural network was trained with 2,026 damage images and tested with 508 damage images. As a result, we completed an algorithm with average mean intersection over union of 67.04% and F1 score of 52.65%. It is expected that the proposed damage detection algorithm could apply to accurate facility condition diagnosis in the near future.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.25 no.4
• /
• pp.245-257
• /
• 2023

The representative crop in the Republic of Korea, rice, is cultivated over extensive areas every year, which resulting in reduced resistance to pests and diseases. One of the major rice diseases, rice blast disease, can lead to a significant decrease in yields when it occurs on a large scale, necessitating early detection and effective control of rice blast disease. Drone-based crop monitoring techniques are valuable for detecting abnormal growth, but frequent image capture for potential rice blast disease occurrences can consume significant labor and resources. The purpose of this study is to early detect rice blast disease using remote sensing data, such as drone and satellite images, along with weather data. Satellite images was helpful in identifying rice cultivation fields. Effective detection of paddy fields was achieved by utilizing vegetation and water indices. Subsequently, air temperature, relative humidity, and number of rainy days were used to calculate the risk of rice blast disease occurrence. An increase in the risk of disease occurrence implies a higher likelihood of disease development, and drone measurements perform at this time. Spectral reflectance changes in the red and near-infrared wavelength regions were observed at the locations where rice blast disease occurred. Clusters with low vegetation index values were observed at locations where rice blast disease occurred, and the time series data for drone images allowed for tracking the spread of the disease from these points. Finally, drone images captured before harvesting was used to generate spatial information on the incidence of rice blast disease in each field.

• Korean Journal of Remote Sensing
• /
• v.30 no.6
• /
• pp.777-786
• /
• 2014

Mt. Ontake is the second highest volcano in Japan. On 02:52 Universal Time Coordinated(UTC), 27th September 2014, Ontake volcano began on the large eruption without notice. Due to the recent eruption, 55 people were killed and around 70 people injured. Therefore, This paper performed numerical experiment to analyse damage effect of volcanic ash corresponding to Ontake volcano erupt. The forecast is based on the outputs of the HYSPLIT Model for volcanic ash. This model, which is based on the UM numerical weather prediction data. Also, a quantitative analysis of the ash dispersion area, it has been detected using satellite images from optical Communication, Ocean and Meterological Satellite-Geostationary Ocean Color Imager (COMS-GOCI) images. Then, the GOCI detected area and simulated ash dispersion area were compared and verified. As the result, the similarity showed the satisfactory result between the detected and simulated area. The concordance ratio between the numerical simulation results and the GOCI images was 38.72 % and 13.57 %, Also, the concordance ratio between the JMA results and the GOCI images was 9.05 % and 11.81 %. When the volcano eruptions, volcanic ash range of damages are wide more than other volcanic materials. Therefore, predicting ash dispersion studies are one of main way to reduce damages.