• Journal of Korean Society of Transportation
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• v.20 no.5
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• pp.163-174
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• 2002

Speeding has been reported as one of the major causes for fatal traffic accidents in Korea. The resolution against this dangerous speeding comes to make the automated speed enforcement system an enforcement tool. The speed detection device, which measures speeds of each incoming vehicles using double loop sensors, requires high accuracy. The object of this study is to develop an accurate speed measurement algorithm using double loop detectors. Some important findings are summarized as follows: 1) It was found that speed measurement errors are caused by scanning rate, distance of two loops, irregular vehicle trajectories, multiple vehicles in detection zone. 2) A proposed algorithm using two signal set proved to reduce variance as well as mean of speed measurement. 3) A proposed filtering algorithm was effective to filter irregular driving vehicles and multiple vehicles in detection zone. A comprehensive field test of developed algorithm resulted in significant improvement of speed measurement accuracy.

• Journal of Korean Society of Transportation
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• v.27 no.4
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• pp.175-181
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• 2009

Intelligent Transportation Systems(ITS) enables road users to enhance efficiency of their trips in a variety of traffic conditions. As a significant part of ITS, information communication technology among vehicles and between vehicles and infrastructure has been being developed to upgrade current traffic data collection technology through location-based traffic surveillance systems. A wider and detailed range of traffic data can be acquired with ease by the technology. However, its performance level falls with environmental impediments such as large vehicles, buildings, harsh weather, which often bring about wireless communication failure. For imputation of vehicle trajectory data discontinued by the failure, several potential existing methods were reviewed and a new method to complement them was devised. AIMSUN API(Application Programming Interface) software was utilized to simulate vehicle trajectories data and missing vehicle trajectories data was randomly generated for the verification of the method. The method was proven to yield more accurate and reliable traffic data than the existing ones.

• Transactions of the Korean Society of Automotive Engineers
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• v.20 no.2
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• pp.135-140
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• 2012

Fuel Cell Hybrid Vehicles (FCHVs) have already become the subject of major interest among automotive industry as well as power management strategies of FCHVs, as the fuel economy of FCHVs depends on them. There are several types of power management strategies of FCHVs that have been developed to improve the fuel economy of FCHVs. Among them, optimal control theory is applied to this study. A problem is defined and its objective is to minimize the energy consumption of an FCHV and to find the optimal trajectories of powertrain parameters during driving. Necessary conditions for the optimal control are introduced and the simulation results of constant costate are compared to that of variable costate in order to prove that the variable costate can be replaced with the constant costate.

• International Journal of Control, Automation, and Systems
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• v.6 no.3
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• pp.351-363
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• 2008

This paper is concerned with tasking and guidance of networked airborne sensors to achieve fault-tolerant sensing. The sensors are coordinated to locate hostile transmitters by intercepting and processing their signals. Faults occur when some sensor-carrying vehicles engaged in target location missions are lost. Faults effectively change the network architecture and therefore degrade the network performance. The first objective of the paper is to optimally allocate a finite number of sensors to targets to maximize the network life and availability. To that end allocation policies are solved from relevant Markov decision problems. The sensors allocated to a target must continue to adjust their trajectories until the estimate of the target location reaches a prescribed accuracy. The second objective of the paper is to establish a criterion for vehicle guidance for which fault-tolerant sensing is achieved by incorporating the knowledge of vehicle loss probability, and by allowing network reconfiguration in the event of loss of vehicles. Superior sensing performance in terms of location accuracy is demonstrated under the established criterion.

• International Journal of Contents
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• v.10 no.2
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• pp.9-17
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• 2014

Since vehicles' trajectories are so complex and dense traffic changes in nature frequently, the VANET (Vehicles Ad-hoc Network), using Omni-directional Antenna, has many channel collisions (or overlapping) on Data Link phrases (MAC layers). It is not easy to keep a good seamless communication status for VANET because of its unpredictable network environment. Among VANET research, Directional Antenna have been proposed as one of the most common systematical solutions to reduce (or to mitigate) this miss-communication problems by narrowing communicational ranges and making use of its customized error-detection process. However, even though Directional Antennas help VANET keep good seamless communication, many VANET researchers have reported that Directional VANET still has miss-communicational problems - this has lead to problems like 'Directional Hidden Terminal Problem', 'Deafness', 'Un-accuracy Lobe Scopes' and 'High Deployment Cost' being reported in various papers. To establish well-organized design assessments for a good Directional VANET MAC protocol to overcome these problems, we rearranged and grouped current Directional VANET' qualitative criteria from several current survey papers using these categories- 'Directional Discovery', 'Directional Forwarding' and 'Directional Handover'. In addition, based on the results of the following analysis, we show the essential design concerns that need to be looked at in order to develop a well-designed Directional-VANET MAC protocol.

• Journal of the Korean Society of Propulsion Engineers
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• v.25 no.3
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• pp.101-112
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• 2021

SpaceX shows various strategies such as constructing various payload portfolio through the reuse of Falcon 9 and Falcon Heavy, constructing the launch vehicles using one type of engine, the transition from kerosene engine to methane engine, and the use of 3D printing. In this study, launch vehicle proposals that can cover a variety of payloads and trajectories from KOMPSAT to GEO-KOMPSAT were constructed, and ten launch vehicles using kerosene gas generator cycle engine, kerosene staged-combustion cycle engine, and methane staged-combustion cycle engine were reviewed. Of the ten launch vehicles, the reusable launch vehicle using a 35-ton methane engine was rated as the best in terms of development potential.

• Information and Communications Magazine
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• v.29 no.8
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• pp.72-84
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• 2012

This paper introduces three vehicle trajectory-based data forwarding schemes, tailored for vehicular ad hoc networks. Nowadays GPS-based navigation systems are popularly used for providing efficient driving paths for drivers. With the driving paths called vehicle trajectories, we can make data forwarding schemes more efficient, considering the micro-scoped mobility of individual vehicles in road networks as well as the macro-scoped mobility of vehicular traffic statistics. This paper shows why the vehicle trajectory is a key ingredient in the design of the vehicle-to-infrastructure, infrastructure-to-vehicle, and vehicle-to-vehicle data forwarding schemes over multihop. Through the mathematical formulation, the key design techniques are shown for three forwarding schemes based on vehicle trajectory, compared with a state-of- the- art data forwarding scheme based on only vehicular traffic statistics.

• Journal of Korea Spatial Information System Society
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• v.10 no.3
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• pp.67-78
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• 2008

With the rapid development of wireless communication and mobile equipment, many applications for location-based services have been emerging. Moving objects such as vehicles and ships change their positions over time. Moving objects have their moving path, called the trajectory, because they move continuously. To monitor the trajectory of moving objects in a large scale database system, an efficient Indexing scheme to processed queries related to trajectories is required. In this paper, we focus on the issues of minimizing the dead space of index structures. The Minimum Bounding Boxes (MBBs) of non-leaf nodes in trajectory-preserving indexing schemes have large amounts of dead space since trajectory preservation is achieved at the sacrifice of the spatial locality of trajectories. In this thesis, we propose entry relocating techniques to reduce dead space and overlaps in non-leaf nodes. we present performance studies that compare the proposed index schemes with the TB-tree and the R*-tree under a varying set of spatio-temporal queries.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.1
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• pp.209-220
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• 2022

With the worldwide spread of African swine fever, interest in livestock epidemics has increased. Livestock transport vehicles are the main cause of the spread of livestock epidemics, but there are no empirical quarantine procedures and standards related to the mobility of livestock transport vehicles in South Korea. This study extracted the trajectory of livestock-related vehicles using the facility-visit history data from the Korea Animal Health Integrated System and the DTG (Digital Tachograph) data from the Korea Transportation Safety Authority. The results are presented as exposure indices aggregating the link-time occupancy of each vehicle. As a result, 274,519 livestock-related vehicle trajectories were extracted, and the exposure values by link and zone were derived quantitatively. This study highlights the need for prior monitoring of livestock transport vehicles and the establishment of post-disaster prevention policies.

• International Journal of Aeronautical and Space Sciences
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• v.11 no.4
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• pp.326-337
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• 2010

This work addresses problems regarding trajectory planning for unmanned aerial vehicle sensors. Such sensors are used for taking measurements of large nonlinear systems. The sensor investigations presented here entails methods for improving estimations and predictions of large nonlinear systems. Thoroughly understanding the global system state typically requires probabilistic state estimation. Thus, in order to meet this requirement, the goal is to find trajectories such that the measurements along each trajectory minimize the expected error of the predicted state of the system. The considerable nonlinearity of the dynamics governing these systems necessitates the use of computationally costly Monte-Carlo estimation techniques, which are needed to update the state distribution over time. This computational burden renders planning to be infeasible since the search process must calculate the covariance of the posterior state estimate for each candidate path. To resolve this challenge, this work proposes to replace the computationally intensive numerical prediction process with an approximate covariance dynamics model learned using a nonlinear time-series regression. The use of autoregressive time-series featuring a regularized least squares algorithm facilitates the learning of accurate and efficient parametric models. The learned covariance dynamics are demonstrated to outperform other approximation strategies, such as linearization and partial ensemble propagation, when used for trajectory optimization, in terms of accuracy and speed, with examples of simplified weather forecasting.