• Journal of Ocean Engineering and Technology
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• v.25 no.1
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• pp.27-31
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• 2011

The previously developed method of estimating the propulsion performance of a UUV was applied to the high speed UUV, which is propelled by commercial thrusters. The thrusters were selected with an overdesign mentality; in other words, their capacities were excessive. At that point, the designer's concern was focused on a question regarding at what rpm the UUV could reach the design speed. Because the developed method required thrust coefficient curve data, the researchers asked for the POW data of the thrusters from the manufacturer. From the data, the researchers extracted the thrust coefficient and estimated the rpm value of design speed for the UUV. Finally, the researchers compared the estimated value and the result from a self-propulsion test using a VPMM (Vertical planar motion mechanism) test at a towing tank in MOERI.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.4B
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• pp.170-177
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• 2005

Due to the redundant rebroadcast packets, network-wide broadcasting is a costly operation in wireless mobile ad hoc networks. To reduce this redundancy, most of previous approaches implicitly or explicitly require periodic refreshing of neighborhood information which continuously imposes additional broadcast overheads. In this paper, we propose a practical broadcast pruning scheme based on the local prediction of a remained coverage area. As the proposed scheme uses only information available in the on-going broadcast process, it can minimize the overheads prevalent in previous approaches.

• Journal of Broadcast Engineering
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• v.28 no.2
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• pp.230-237
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• 2023

Self-driving is a key technology of the fourth industry and can be applied to various places such as cars, drones, cars, and robots. Among them, localiztion is one of the key technologies for implementing autonomous driving as a technology that identifies the location of objects or users using GPS, sensors, and maps. Locilization can be made using GPS or LIDAR, but it is very expensive and heavy equipment must be mounted, and precise location estimation is difficult for places with radio interference such as underground or tunnels. In this paper, to compensate for this, we proposes an image retrieval using attention module and image segmentation maps using color images acquired with low-cost vision cameras as an input.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.12
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• pp.561-568
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• 2021

In recent years, autonomous driving technologies have become a high-value-added technology that attracts attention in the fields of science and industry. For smooth Self-driving, it is necessary to accurately detect an object and estimate its movement speed in real time. CNN-based deep learning algorithms and conventional dense optical flows have a large consumption time, making it difficult to detect objects and estimate its movement speed in real time. In this paper, using a single camera image, fast object detection was performed using the YOLOv5 algorithm, a deep learning algorithm, and fast estimation of the speed of the object was performed by using a local dense optical flow modified from the existing dense optical flow based on the detected object. Based on this algorithm, we present a system that can predict the collision time and probability, and through this system, we intend to contribute to prevent tram accidents.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.252-263
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• 2016

Road sign is an important street furniture that gives some information such as road conditions, driving direction and condition for a driver. Thus, road sign is a major target of image recognition for self-driving car, ADAS(autonomous vehicle and intelligent driver assistance systems), and ITS(intelligent transport systems). In this paper, an enhanced road sign recognition system is proposed for MMS(Mobile Mapping System) using the single camera and GPS. For the proposed system, first, a road sign recognition scheme is proposed. this scheme is composed of detection and classification step. In the detection step, object candidate regions are extracted in image frames using hybrid road sign detection scheme that is based on color and shape features of road signs. And, in the classification step, the area of candidate regions and road sign template are compared. Second, a Geo-marking scheme for geo-content that is consist of road sign image and coordinate value is proposed. If the serious situation such as car accident is happened, this scheme can protect geographical information of road sign against illegal users. By experiments with test video set, in the three parts that are road sign recognition, coordinate value estimation and geo-marking, it is confirmed that proposed schemes can be used for MMS in commercial area.