• Proceedings of the Korea Electromagnetic Engineering Society Conference
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• 2003.11a
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• pp.203-207
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• 2003

Due to the inherent nature of the low flying UAV, obstacle detection is a fundamental requirement in the flight path to avoid the collision from obstacles as well as manned aircraft. In this paper, a preliminary sensor requirements of an obstacle detection system for UAV in low-altitude flight are analyzed, and the automated obstacle detection sensor system is proposed assessing both passive and active sensors such as EO camera, IR, Laser radar, microwave and millimeter radar. In addition, TCAS (Traffic Alert and Collision Avoidance System) are reviewed for the collision avoidance of the manned aircraft system. It is suggested that small-sized radar sensor is the best candidate for the smart UAV because an active radar can provide the real-time informations on range and range rate in the all-weather environment. However, an important constraints on small UAV should be resolved in terms of accommodation of the mass, volume, and power allocated in the payload of the UAV system design requirements.

• International Journal of Highway Engineering
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• v.14 no.2
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• pp.93-99
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• 2012

This study analyzes traffic data which are collected by VDS(Vehicle Detection System) to research the relationship between spacing distribution and vehicles' relative speed. The collected data are relative speed between preceding and following vehicles, passing time and speed. They are also classified by lane and direction. For the result of the analysis, in the same platoon, we figure out that mean of spacing is 40m, which can be a value to determine section A to D. To compare spacing according to time interval, this study splits time intervals to peak hour and non-peak hour by peak hour traffic volume. In conclusion, vehicles in peak hour are in car following because most drive similar speed as preceding vehicle and they have relatively small spacing. On the other hand, non-peak hour's spacing between vehicles is bigger than that of peak hour. This implies driver's behaviors that the less spacing, the more aggressive and want to reduce their travel time in peak hour, whereas most drive easily in non-peak hour and recreational trip purpose because of less time pressure.

• Proceedings of the Korea Contents Association Conference
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• 2003.11a
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• pp.109-112
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• 2003

A traffic flooding attack is an attack type that interfere with normal service by running out network bandwidth, process throughput, and system resource. It can be recognized intuitively by network slowdown, connect impossibility state and detected more exactly by collecting and analyzing packets that generate traffic flooding. In this paper, the packet analysis scheme is proposed for the more precise detection.

• Korean Journal of Remote Sensing
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• v.37 no.1
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• pp.69-84
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• 2021

Awareness of boats is a main issue in areas of fishery management, illegal fishing, and maritime traffic, etc. For the awareness, Automatic Identification System (AIS) and Vessel-Pass System (V-PASS) have been widely used to collect the boat-related information. However, only using these systems makes it difficult to collect the accurate information. Recently, satellite-based data has been increasingly used as a cooperative system. In 2015, U.S. National Oceanic and Atmospheric Administration (NOAA) developed a boat detection algorithm using Visible Infrared Imaging Radiometer Suite (VIIRS) Day & Night Band (DNB) data. Although the detections have been widely utilized in many publications, it is difficult to estimate the night-time fishing boats immediately. Particularly, it is difficult to estimate the threshold due to the lunar irradiation effect. This effect must be corrected to apply a single specific threshold. In this study, the moon phase was considered as the main frequency of this effect. Considering the moon phase, relational expressions are derived and then used as offsets for relative correction. After the correction, it shows a significant reduction in the standard deviation of the threshold compared to the threshold of NOAA. Through the correction, this study can set a constant threshold every day without determination of different thresholds. In conclusion, this study can achieve the detection applying the single specific threshold regardless of the moon phase.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.4
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• pp.467-473
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• 2020

Recently, the number of traffic accidents has also increased with the increase in the penetration rate of cars in Korea. In particular, not only inter-vehicle accidents but also human accidents near crosswalks are increasing, so that more attention to traffic safety around crosswalks are required. In this paper, we propose a system for predicting the safety level around the crosswalk by recognizing an approaching vehicle and estimating the speed of the vehicle using NVIDIA Jetson Nano-class edge devices. To this end, various machine learning models are trained with the information obtained from deep learning-based vehicle detection to predict the degree of risk according to the speed of an approaching vehicle. Finally, based on experiments using actual driving images and web simulation, the performance and the feasibility of the proposed system are validated.

• Convergence Security Journal
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• v.20 no.5
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• pp.11-17
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• 2020

With the increase in cases of using Bluetooth devices used in the electric vehicle charging systems, security issues are also raised. Although various technical efforts have beed made to enhance security of bluetooth technology, various attack methods exist. In this paper, we propose an intelligent Bluetooth intrusion detection system based on a well-known machine learning method, Hidden Markov Model, for the purpose of detecting intelligently representative Bluetooth attack methods. The proposed approach combines packet types of H4, which is bluetooth transport layer protocol, and the transport directions of the packet firstly to represent the behavior of current traffic, and uses the temporal deployment of these combined types as the final input features for detecting attacks in real time as well as accurate detection. We construct the experimental environment for the data acquisition and analysis the performance of the proposed system against obtained data set.

• Journal of Korean Society of Transportation
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• v.13 no.3
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• pp.67-86
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• 1995

It requires the detector system which can collect highly reliable traffic data in order to perform the real-time traffic signal control. This study is to decide the optimal shape of inductive loop for the real-time traffic signal control .This loop is located at the stopline in the signalized intersection for DS(Degree of Saturation) control. In order to find out the optimal shape of loop, 6types of experiments were performed . The results of the basic experiments of loops are as follows ; -the optimal number of turns for loop is 3 turns. -the impedance values of the loop detectors are similar to that of NEMA standards -the 1.8${\times}$4.5M loop is excellent for sensitivity in actual detection range of car length comparing to other shape of inductive loops. At the experimental of establishments of the optimal loop shape, it found that 1.8 4.5M loop has the highest values of $\DeltaL$ comparing to other types of loops, It means that the range of Lead-in cable length of this loop. And this loop is highly reliable in occpupancy time. Conclusivley, the 1.8${\times}$4.5M inductive loop is the optimal solution as a stop line loop detector for real -time traffic signal control.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.9 s.351
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• pp.69-79
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• 2006

A number of researches on vision-based traffic monitoring system have been carried out. Most of traffic monitoring schemes belong to one of two categories: analyzing of entire traffic scene and examining of local region. However, the proposed methods suffer from severe performance deterioration when applied in different operating conditions because of the loss of robustness. This paper is aimed at surveying various methods proposed and analyzing the advantages and disadvantages of these methods. Also we propose and investigate appropriate approaches to solve the problems in specific applications.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.7
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• pp.2512-2531
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• 2014

We propose a new Distributed Denial of Service (DDoS) defense mechanism that protects http web servers from application-level DDoS attacks based on the two methodologies: whitelist-based admission control and busy period-based attack flow detection. The attack flow detection mechanism detects attach flows based on the symptom or stress at the server, since it is getting more difficult to identify bad flows only based on the incoming traffic patterns. The stress is measured by the time interval during which a given client makes the server busy, referred to as a client-induced server busy period (CSBP). We also need to protect the servers from a sudden surge of attack flows even before the malicious flows are identified by the attack flow detection mechanism. Thus, we use whitelist-based admission control mechanism additionally to control the load on the servers. We evaluate the performance of the proposed scheme via simulation and experiment. The simulation results show that our defense system can mitigate DDoS attacks effectively even under a large number of attack flows, on the order of thousands, and the experiment results show that our defense system deployed on a linux machine is sufficiently lightweight to handle packets arriving at a rate close to the link rate.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.169-176
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• 2021

Maritime monitoring requirements have been beyond human operators capabilities due to the broadness of the coverage area and the variety of monitoring activities, e.g. illegal migration, or security threats by foreign warships. Abnormal vessel movement can be defined as an unreasonable movement deviation from the usual trajectory, speed, or other traffic parameters. Detection of the abnormal vessel movement requires the operators not only to pay short-term attention but also to have long-term trajectory trace ability. Recent advances in deep learning have shown the potential of deep learning techniques to discover hidden and more complex relations that often lie in low dimensional latent spaces. In this paper, we propose a deep autoencoder-based clustering model for automatic detection of vessel movement anomaly to assist monitoring operators to take actions on the vessel for more investigation. We first generate gridded trajectory images by mapping the raw vessel trajectories into two dimensional matrix. Based on the gridded image input, we test the proposed model along with the other deep autoencoder-based models for the abnormal trajectory data generated through rotation and speed variation from normal trajectories. We show that the proposed model improves detection accuracy for the generated abnormal trajectories compared to the other models.