• Journal of Broadcast Engineering
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• v.27 no.6
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• pp.936-939
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• 2022

As air combat system technologies developed in recent years, the development of air defense systems is required. In the operating concept of the anti-aircraft defense system, selecting an appropriate armament for the target is one of the system's capabilities in efficiently responding to threats using limited anti-aircraft power. Much of the flying threat identification relies on the operator's visual identification. However, there are many limitations in visually discriminating a flying object maneuvering high speed from a distance. In addition, as the demand for unmanned and intelligent weapon systems on the modern battlefield increases, it is essential to develop a technology that automatically identifies and classifies the aircraft instead of the operator's visual identification. Although some examples of weapon system identification with deep learning-based models by collecting video data for tanks and warships have been presented, aerial vehicle identification is still lacking. Therefore, in this paper, we present a model for classifying fighters, helicopters, and drones using a convolutional neural network model and analyze the performance of the presented model.

• Geophysics and Geophysical Exploration
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• v.25 no.1
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• pp.26-37
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• 2022

Recently, multiple methods using small aircraft for geophysical exploration have been suggested as a result of the development of information and communication technology. In this study, we introduce the hybrid unmanned aircraft electromagnetic system of the Korea Institute of Geosciences and Mineral resources, which is under development. Additionally, data processing and interpretation methods are suggested via the analysis of datasets obtained using the system under development to verify the system. Because the system uses a three-component receiver hanging from a drone, the effects of rotation on the obtained data are significant and were therefore corrected using a rotation matrix. During the survey, the heights of the source and the receiver and their offsets vary in real time and the measured data are contaminated with noise. The noise makes it difficult to interpret the data using the conventional method. Therefore, we developed a recurrent neural network (RNN) model to enable rapid predictions of the apparent resistivity using magnetic field data. Field data noise is included in the training datasets of the RNN model to improve its performance on noise-contaminated field data. Compared with the results of the electrical resistivity survey, the trained RNN model predicted similar apparent resistivities for the test field dataset.

• Journal of the Korean Geotechnical Society
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• v.40 no.3
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• pp.41-54
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• 2024

Regular monitoring plays a crucial role in ensuring the safety of geotechnical structures. Currently, nondestructive methods are employed to monitor such structures to minimize the impact, e.g., sensor-based accelerometers, displacement meters, image-based lasers, and drone imaging. These technologies can observe surface changes; however, they frequently suffer difficulties in terms of identifying changes in internal properties. To monitor changes in internal properties, in situ geotechnical investigations can be employed. A nondestructive test that can be used for this purpose is the spectral analysis of surface wave (SASW) test using geophones. The SASW test is a nondestructive method; however, due to the time required for data interpretation and the difficulty in analyzing the data, it is challenging to use the SASW test for monitoring applications that require frequent observations. However, it is possible to apply the first-step analysis, which yields the dispersion curve, for monitoring rather than the complete SASW analysis, which yields the shear wave velocity. Thus, this paper presents a fundamental study on the phase difference that derives the dispersion curve to utilize the SASW test for monitoring. The reliability of each phase difference interval is examined to determine the boundary to the subjected monitor. The study used phase difference data obtained using a geophone from a single-layered, homogeneous ground site to evaluate reliable boundaries. The findings of this study are expected to improve the utility of monitoring by identifying the ideal boundary for phase difference data.

• Ecology and Resilient Infrastructure
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• v.6 no.1
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• pp.34-48
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• 2019

Naeseong Stream is a natural sand-bed river that flows through mountainous and cultivated area in northern part of Gyeongbuk province. It had maintained its inherent landscape characterized by white sandbars before 2010s. However, since then changes occurred, which include construction of Yeongju Dam and the extensive vegetation development around 2015. In this study, long-term monitoring was carried out on Naeseong Stream to analyze these changes objectively. This paper aims to provide a dataset of the investigation on channel morphology and vegetation for the period 2012-2018. Methods of investigation include drone/terrestrial photography, LiDAR aerial survey and on-site fieldwork. The main findings are as follows. Vegetation development in the channel of Naeseong Stream began around 1987. Before 2013 it occurred along the downstream reach and since then in the entire reach. Some of the sites where riverbed is covered with vegetation during 2014~2015 were rejuvenated to bare bars due to the floods afterwards, but woody vegetation was established in many sites. Bed changes occurred due to deposition of sediment on the vegetated surfaces. Though Naeseong Stream has maintained its substantial sand-bed characteristics, there has been a slight tendency in bed material coarsening. Riverbed degradation at the thalweg was observed in the surveyed cross sections. Considering all the results together with the hydrological characteristics mentioned in the precedent paper (I), it is thought that the change in vegetation and landscape along Naeseong Stream was mainly due to decrease of flow. The effect of Yeongju Dam on the change of the riverbed degradation was briefly discussed as well.

• Economic and Environmental Geology
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• v.54 no.6
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• pp.717-731
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• 2021

We analyzed the characteristics of the habitat environment for the Seonam study area in Ulsan, the southern shore of the East Sea using bathymetry and seafloor environment data. The depth of the study area ranges from about 0 m to 23 m. In the west of the study area, the water depth is shallow with a gentle slope, and the water depth becomes deeper with a steep slope in the east. Due to the right-lateral strike-slip faults located in the continental margin of the East Sea, the fracture surfaces of the seabed rocks are mainly in the N-S direction, which is similar to the direction of the strike faults. Three seafloor types (conglomeratic-grained sandy, coasre-graiend sandy, fine-grained sandy) and rocky bottom area have been classified according to the analyses of the bathymerty, seafloor image, and surface sediment data. The rocky bottom areas are mainly distributed around Seaoam and in the northern and southern coastal area. But the intermediate zone between Seonam and coastal area has no rocky bottom. This intermediate area is expected to have active sedimentation as seawater way. The sandy sediments are widely distributed throughout the study area. Underwater images and UAV images show that Cnidarians, Brachiopods, Mollusks are mostly dominant in the shallow habitat and various Nacellidae, Mytilidae live on the intertidal zone around Seonam. Annelida and Arthropod are dominant in the sandy sediments. The distribution of marine organism in the study area might be greatly influenced by the seafloor type, the composition and particle size distribution of the seafloor sediments. The analysis of habitat environment mapping with bathymetry, seafloor data and underwater images is supposed to contribute to the study of the structure and function of marine ecosystem.

• Korean Security Journal
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• no.60
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• pp.113-135
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• 2019

Despite being one of the most important national facilities, the National Assembly building of the Republic of Korea has become increasingly vulnerable to potential terrorist attacks, and the instances of demonstrations occurring around and banned items taken into the building are continuing to rise. In addition, promoting the idea of "open assembly" has led to increased visitors and weakened access control. Furthermore, while there is a significant symbolic value attached to attacking the National Assembly building, the level of security management is relatively very low, making it a suitable target for terrorism. In order to address such vulnerability, an appropriate access control system should be in place from the areas surrounding the building. However, the National Assembly Security Service which oversees security around the building is scheduled to disband in June 2020 following the abolition of the conscripted police force in 2023. Therefore, there needs to be an alternative option to bolster the security system outside the facility. In this research, the perceptions of 114 government officials in charge of security at the National Assembly Secretariat toward the protection and security system of the areas surrounding the National Assembly building were examined. Results showed that the respondents believed it was highly likely that risky situations could occur outside the building, and the use of advanced technologies such as intelligent video surveillance, intrusion detection system, and drones was viewed favorably. Moreover, a mid- to long-term plan of establishing a unified three-layer protection system and designating a department in charge of the security outside the building were perceived positively. Lastly, the participants supported the idea of employing private police to replace the National Assembly Security Service for the short term and introducing parliamentary police for the mid- to long-term.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.4
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• pp.353-361
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• 2020

Due to the rapid urbanization, various traffic problems such as traffic jams during commute and regular traffic jams are occurring. In order to solve these traffic problems, it is necessary to quickly and accurately estimate and analyze traffic volume. ITS (Intelligent Transportation System) is a system that performs optimal traffic management by utilizing the latest ICT (Information and Communications Technology) technologies, and research has been conducted to analyze fast and accurate traffic volume through various techniques. In this study, we proposed a deep learning-based vehicle detection method using UAV (Unmanned Aerial Vehicle) video for real-time traffic analysis with high accuracy. The UAV was used to photograph orthogonal videos necessary for training and verification at intersections where various vehicles pass and trained vehicles by classifying them into sedan, truck, and bus. The experiment on UAV dataset was carried out using YOLOv3 (You Only Look Once V3), a deep learning-based object detection technique, and the experiments achieved the overall object detection rate of 90.21%, precision of 95.10% and the recall of 85.79%.

• Korean Security Journal
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• no.61
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• pp.9-38
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• 2019

As the scales & density of the Korean national infrastructures have been increased, they will be identified as rich and attractive potential targets for intensified North Korea's attack in the rear region and terrorism attack. In addition, due to changes in security environment such as drone threats and lack of security forces under the 52-hour workweek law, I think that it is the proper time point to reevaluate the effectiveness and appropriateness of the current physical protection system and its shift to a new system. In this study, the direction and improvement of the perimeter physical protection systems of the national infrastructures are to be studied from the viewpoints of its concepts of operations and design methodology, focusing on the nuclear power plant. The reason why we focus on nuclear power plants is because they cause wide-range and long-term damages caused by radioactive materials disperal and pollution, along with short-term damage caused by the interruption of electricity generation in the event of damage to nuclear power plants. With the aim of extracting improvement directions, as we will comprehensively review domestic research trends and domestic·overseas related laws, and consider Korea's specificity, we try to reframe the concept of operation - systematization, mobilization and flexibility -, and establish criteria on system change. In order to improve the technical performance of the new perimeter physical protection system, we study on high-fidelity·multi-methodology based integrated design methodology, breaking from individual silo-type design methods, and I suggest improvement of government procurement, its expansion to export business and other national infrastructure.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.1
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• pp.140-153
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• 2019

Cloase-range image information from drones and ground-based camera has been frequently used in the field of disaster mitigation with 3D modeling and mapping. In addition, the utilization of virtual reality(VR) is being increased by implementing realistic 3D models with the VR technology simulating disaster circumstances in large scale. In this paper, we created a VR training program by extracting realistic 3D models from close-range images from unmanned aircraft and digital camera on hand and observed several issues occurring during the implementation and the effectiveness in the case of a VR application in training for disaster mitigation. First of all, we built up a scenario of disaster and created 3D models after image processing with the close-range imagery. The 3D models were imported into Unity, a software for creation of augmented/virtual reality, as a background for android-based mobile phones and VR environment was created with C#-based script language. The generated virtual reality includes a scenario in which the trainer moves to a safe place along the evacuation route in the event of a disaster, and it was considered that the successful training can be obtained with virtual reality. In addition, the training through the virtual reality has advantages relative to actual evacuation training in terms of cost, space and time efficiencies.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.224-226
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• 2016

With the technical development and rapid increase of private demand, the new market for unmanned vehicle combined with the characteristics of 'unmanned automation' and 'vehicle' is rapidly growing. Even though the pilot driving is currently allowed in some countries, there is no country that has institutionalized the formal driving of self-driving cars. In case of the existing vehicles, safety incidents are frequently happening due to the frequent malfunction of the rear sensor, blind spot of the rear camera, or drivers' carelessness. Once such minor flaws are complemented, the relevant regulations for the commercialization of self-driving car and small drone could be relieved. Contrary to the ultrasonic and laser sensors used for the existing vehicles, this paper aims to attempt the distance measurement by using the depth sensor. A depth camera calculates the distance data based on the TOF method calculating the time difference by lighting laser or infrared light onto an object or area and then receiving the beam coming back. As this camera can obtain the depth data in the pixel unit of CCD camera, it can be used for collecting depth data in real-time. This paper suggests to solve problems mentioned above by using depth data in real-time and also to design the obstacle avoidance system through distance measurement.