• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.12
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• pp.2636-2641
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• 2010

To support anti-collision between ship to ship and sea-search and sea-rescue work, ship automatic identification system(AIS) that can both send and receive messages between ship and VTS Traffic control have been adopted. And port control system can control traffic vessel service which is co-operated with AIS. For more efficient traffic vessel service, ship recognition and display system is required to cooperated with AIS. In this paper, we propose ship detection system which is co-operated with AIS by using background estimation based on image processing for on the sea or harbor image extracted from camera. We experiment with on the sea or harbor image extracted from real-time input image from camera. By computer simulation and real world test, the proposed system show more effective to ship monitoring.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.687-690
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• 2015

Forward collision warning systems(FCWS) and lane change assist systems(LCAS) need regions of interest for detecting lanes and objects as road regions. Watershed segmentation is effective algorithm that classify the road. That algorithm is split results appear differently depending on Watershed line with local minimum in the early part of the seed. If not road regions or vehicles combined the road's seed, It segment road with the others. For compensate the that defect, It has to adaptive change by road environment. The method is that image segmentate the several of regions of interest. Then It is set in a straight line that is detected in regions of interest. If It was detected cars on seed, seed is adjusted the location. And If It wasn't include the line, seed is adjusted the length for final decision the seed. We can detect the road region using the final seed that selected according to the road environment.

• The Journal of Information Technology
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• v.7 no.3
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• pp.37-45
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• 2004

The representative communication method which is applied in the low-speed power line communication modem with 60bps is single carrier method. It has been used mainly for the control. The single carrier method is very sensitive to a power line communication channel environment. Specially, the severe attenuation of the transmission signal according to the notch characteristics of channel becomes the main cause of communication error. Domestic power line channel environment has this notable feature. In this paper, we implemented the low-speed power line communication system which used the chirp signal method to be strong in notch and noise characteristics. In this research, we proposed the method which transmits 1- '1 Unit symbol Chirp signal' with a 100${\mu}s$ time within 1ms for 1 bit. Also it applied for the Convolution code for an error correction and the Manchester code for a collision perception and an error detection. It used the method which uses the bit correlator for signal detection in the receiver parts. We confirmed that the communication method of the chirp method has a excellent performance compared to single carrier methods with a result of experiment of the low-speed power line communication system with the 60bps.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.1
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• pp.63-73
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• 2021

This paper presents a algorithm for automatic target recognition robust to the influence of the flame in order to track the target by EOTS(Electro-Optical Targeting System) equipped on UAV(Unmanned Aerial Vehicle) when there is aerial target or marine target with flame at the same time. The proposed method converts infrared images of targets and flames into a gradient vector field, and applies each gradient magnitude to a polynomial curve fitting technique to extract polynomial coefficients, and learns them in a shallow neural network model to automatically recognize targets and flames. The performance of the proposed technique was confirmed by utilizing the various infrared image database of the target and flame. Using this algorithm, it can be applied to areas where collision avoidance, forest fire detection, automatic detection and recognition of targets in the air and sea during automatic flight of unmanned aircraft.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.328-335
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• 2022

With the advance of robot capabilities and functionalities, construction robots assisting construction workers have been increasingly deployed on construction sites to improve safety, efficiency and productivity. For close-proximity human-robot collaboration in construction sites, robots need to be aware of the context, especially construction worker's behavior, in real-time to avoid collision with workers. To recognize human behavior, most previous studies obtained 3D human poses using a single camera or an RGB-depth (RGB-D) camera. However, single-camera detection has limitations such as occlusions, detection failure, and sensor malfunction, and an RGB-D camera may suffer from interference from lighting conditions and surface material. To address these issues, this study proposes a novel method of 3D human pose estimation by extracting 2D location of each joint from multiple images captured at the same time from different viewpoints, fusing each joint's 2D locations, and estimating the 3D joint location. For higher accuracy, the probabilistic representation is used to extract the 2D location of the joints, considering each joint location extracted from images as a noisy partial observation. Then, this study estimates the 3D human pose by fusing the probabilistic 2D joint locations to maximize the likelihood. The proposed method was evaluated in both simulation and laboratory settings, and the results demonstrated the accuracy of estimation and the feasibility in practice. This study contributes to ensuring human safety in close-proximity human-robot collaboration by providing a novel method of 3D human pose estimation.

• The Journal of the Korea Contents Association
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• v.16 no.10
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• pp.588-597
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• 2016

According to an annual industrial accident report from Ministry of Employment of Labor, among the various types of accidents, the number of accidents from construction industry increases every year with the percentage of 27.56% as of 2014. In fact, this number has risen almost 3% over the last four years. Currently, among the industrial accidents, heavy machinery causes most of the tragedy such as collision or narrowness. As reported by the government, most of the time, both heavy machinery drivers and workers were unaware of each other's positions. Nowadays, however when society requires highly complex structures in minimal time, it is inevitable to allow heavy construction equipments running simultaneously in a construction field. In this paper, we have developed Approach Detection System for excavator in order to reduce the increasing number. The imaged based Approach Detection System contains camera, approach detection sensor and Around View Monitor (AVM). This system is also applicable in a small scale construction fields along with other machineries besides excavators since this system does not require additional communication infra such as server.

• Journal of the Korea Society of Computer and Information
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• v.16 no.9
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• pp.69-76
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• 2011

According to the related reports, about 60 percents of ship collisions have resulted from operating mistake caused by human factor. Specially, the report said that negligence of observation caused 66.8 percents of the accidents due to a human factor. Hence automatic detection and tracking of an object from an IR images are crucial for safety navigation because it can relieve officer's burden and remedies imperfections of human visual system. In this paper, we present a method to detect an object such as ship, rock and buoy from a sea IR image. Most edge directions of the sea image are horizontal and most vertical edges come out from the object areas. The presented method uses them as a characteristic for the object detection. Vertical edges are extracted from the input image and isolated edges are eliminated. Then morphological closing operation is performed on the vertical edges. This caused vertical edges that actually compose an object be connected and become an object candidate region. Next, reference object regions are extracted using horizontal edges, which appear on the boundaries between surface of the sea and the objects. Finally, object regions are acquired by sequentially integrating reference region and object candidate regions.

• Journal of information and communication convergence engineering
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• v.4 no.4
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• pp.141-146
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• 2006

In these days, wireless data services are becoming ubiquitous in our daily life because they offers several fundamental benefits including user mobility, rapid installation, flexibility, and scalability. Moreover, the requests for various multimedia services and the Quality of Service (QoS) support have been one of key issues in wireless data communications. Therefore the research relative to Medium Access Control (MAC) has been progressing rapidly. Especially a number of QoS-aware MAC schemes have been introduced to extend the legacy IEEE 802.11 MAC protocol which has not guaranteed any service differentiation. However, none of those schemes fulfill both QoS features and channel efficiency although these support the service differentiation based on priority. Therefore this paper studies a novel MAC scheme, referred to as Enhanced Distributed Coordination Function with Network Adaptation (EDCF-NA), for enhancements of both QoS and medium efficiency. It uses a smart factor denoted by ACK rate and Network Load Threshold (TH). In this paper, we study how the value of TH has effect on MAC performance and how the use of optimal TH pair improves the overall MAC performance in terms of the QoS, channel utilization, collision rate, and fairness. In addition, we evaluate and compare both the performance of EDCF-NA depending on several pairs of TH and the achievement of various MAC protocols through simulations by using Network Simulator-2 (NS-2).

• Transactions of the Korean Society of Automotive Engineers
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• v.17 no.2
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• pp.34-41
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• 2009

The latest vehicle yields a superior safety and reduction of driving burden by monitoring the driving state of vehicle and its environment with various sensors. To detect other vehicles and objects of the rear left and right-side blind spot area of driver, provide the information about a existence of objects inside the blind spot, and give a signal to avoid collision, this study proposes the intelligent outside rear-view mirror system. This task has substantially complicated several factors. For example, the size, geometry and features of the various vehicles which might enter the monitored zone is varied widely and therefore present various reflective characteristics. This study proposes the optimal specification and configuration of optical system and IR array sensor of blind spot detection system, and shows the results of the performance evaluation of developed system.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.2
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• pp.205-214
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• 2004

This paper presents design concepts, specifications and performances of a newly developed Black Box, the reconstruction analysis tool with the records, and results of validation tests. The Black Box can detect crash accidents automatically, and record the vehicle's motion and driver's maneuvers during a pre-defined time period before and after the accident. The items of the Black Box included the acceleration, yaw-rate, vehicle speed, engine RPM, braking application, steering and several digital inputs for recording driver's maneuvers. To detect the accident-related-crash, it is important to understand characteristics of the crash signal, which are much different from those of normal driving. Therefore, analytical considerations should be taken in designing pre-filtering circuits and selecting appropriate parameters for identifying crash accidents. And, it is necessary to select proper combination of motion sensors and design proper pre-filtering circuits in order to describe the vehicle's motion. The analysis algorithms were developed and implemented which can perform accurate detection of crash accidents, simulating pre-crash trajectories, and calculating parameters for reconstruction analysis of crash accidents. The developed Black Box was installed on passenger cars and several types of validation tests were conducted. Through the tests, the accuracy of the recorded data and usefulness of the analysis tool for reconstruction have been validated.