• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
• /
• 2000.04a
• /
• pp.123-127
• /
• 2000

This paper deals with the ways how the AUV can detect and treat possible emergency situations during the mission. The emergency situations can be divided into two parts according to the zones where the situations take place-inner zone, and outer zone. This paper explains how each element of emergency situation is detected and treated, and as a result, introduce the algorithm of this procedure for the autonomous cruising.

• Proceedings of the Korean Information Science Society Conference
• /
• 2004.04a
• /
• pp.217-219
• /
• 2004

전자문서교환(EDI, Electronic Data Interchange)은 기업과 기업 간에 컴퓨터와 컴퓨터의 통신을 통하여 필요한 거래문서를 구조화된 형식으로 교환하여 업무를 처리하는 방식을 말한다. 이러한 전자문서의 유통은 절러 위험요소로부터 완전히 해방되지는 못한다. 본 연구에서는 향후 국내에서 발생 될 위협요소 중 우선적인 보호가 요구되는 것으로 메시지 노출로 인한 프라이버시 침해 및 중요 내용의 노출문제와 메시지 수정 문제 및 발신처 인증 문제. 그리고 수신자의 수신사실에 대한 부인을 위험요소로 선정하였다. 또한, 이를 막기 위한 보안서비스를 메시지 비밀보장. 무결성, 메시지 발신처 인증 및 수신내용 부인불능 등을 선정하여 이들의 구현방안을 제시하였다.

• The KIPS Transactions:PartC
• /
• v.16C no.5
• /
• pp.621-628
• /
• 2009

Wireless sensor networks (WSNs) have been considered as a promising method for reliably monitoring both civil and military environments under hazardous or dangerous conditions. Due to the special property and difference from the traditional wireless network, the lifetime of the whole network is the most important aspect. The bottleneck nodes widely exist in WSNs and lead to decrease the lifetime of the whole network. In order to find out the bottleneck nodes, the traditional centralized bottleneck detection method MINCUT has been proposed as a solution for WSNs. However they are impractical for the networks that have a huge number of nodes. This paper first proposes a distributed algorithm called DBND (Distributed Bottleneck Node detection) that can reduce the time for location information collection, lower the algorithm complexity and find out the bottleneck nodes quickly. We also give two simple suggestions of how to solve the bottleneck problem. The simulation results and analysis show that our algorithm achieves much better performance and our solutions can relax the bottleneck problem, resulting in the prolonging of the network lifetime.

• Journal of Internet Computing and Services
• /
• v.21 no.2
• /
• pp.27-37
• /
• 2020

Recently, big data is rapidly emerging as a core technology in the 4th industrial revolution. Further, the utilization and the demand of drones are continuously increasing with the development of the 4th industrial revolution. However, as the drones usage increases, the risk of drones falling increases. Drones always have a risk of being able to fall easily even with small problems due to its simple structure. In this paper, in order to predict the risk of drone fall and to prevent the fall, ESC (Electronic Speed Control) is attached integrally with the drone's driving motor and the acceleration sensor is stored to collect the vibration data in real time. By processing and monitoring the data in real time and analyzing the data through big data obtained in such a situation using a Fast Fourier Transform (FFT) algorithm, we proposed a prediction system that minimizes the risk of drone fall by analyzing big data collected from drones.

• Journal of Navigation and Port Research
• /
• v.28 no.1
• /
• pp.1-8
• /
• 2004

The final goal of this research is to establish the relative dangerousness D/B for factors on seakeeping performance. This D/B is, essential to develope the seakeeping performance evaluation system built-on-ship. The system is composed of the apparatus for measuring a vertical acceleration to be generated by the ship's motions, computer for calculating the synthetic seakeeping performance index and monitor for displaying the evaluating diagram of navigational safety of ship. In this paper, a methodology on the establishment of the relative dangerousness D/B for factors on seakeeping performance is presented by a numerical simulations, playing an important role on the algorithm of the program for calculating the synthetic seakeeping performance index. Finally, It is investigated whether the relative dangerousness D/B can be realized an accurate values according to the loading conditions, weather conditions, wave directions and present ship's speed of a model ship.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2021.07a
• /
• pp.283-284
• /
• 2021

최근 SARS-CoV-2 백신들의 예방접종이 진행됨에 따라 코로나 19 팬데믹의 종결이 예상되고 있다. 하지만 계속해서 출현 중인 변종 바이러스들은 팬데믹 종결의 위험요소로 남아있다. 본 논문에서는 사전학습된 단백질 BERT와 단백질-단백질 상호작용 모델을 활용한 SARS-CoV-2 스파이크 단백질의 변이 예측 분석 알고리즘을 제안한다. 제안하는 기술은 변이 단백질 서열의 예측과 변이 단백질과 human ACE2 수용체의 친화도에 따른 자연선택으로 이루어진다. 이를 통해 시간이 지나며 나타날 수 있는 변종 바이러스들을 시뮬레이션 할 수 있어 변종 바이러스들의 해결에 기여할 것으로 기대된다.

• The KIPS Transactions:PartB
• /
• v.18B no.4
• /
• pp.213-220
• /
• 2011

This paper proposes an effective, four-stage smoke detection method based on video that provides emergency response in the event of unexpected hazards in early fire-alarming systems. In the first phase, an approximate median method is used to segment moving regions in the present frame of video. In the second phase, a color segmentation of smoke is performed to select candidate smoke regions from these moving regions. In the third phase, a feature extraction algorithm is used to extract five feature parameters of smoke by analyzing characteristics of the candidate smoke regions such as area randomness and motion of smoke. In the fourth phase, extracted five parameters of smoke are used as an input for a K-nearest neighbor (KNN) algorithm to identify whether the candidate smoke regions are smoke or non-smoke. Experimental results indicate that the proposed four-stage smoke detection method outperforms other algorithms in terms of smoke detection, providing a low false alarm rate and high reliability in open and large spaces.

• Journal of Internet Computing and Services
• /
• v.21 no.4
• /
• pp.117-126
• /
• 2020

A maritime object detection system is an intelligent assistance system to maritime autonomous surface ship(MASS). It detects automatically floating debris, which has a clash risk with objects in the surrounding water and used to be checked by a captain with a naked eye, at a similar level of accuracy to the human check method. It is used to detect objects around a ship. In the past, they were detected with information gathered from radars or sonar devices. With the development of artificial intelligence technology, intelligent CCTV installed in a ship are used to detect various types of floating debris on the course of sailing. If the speed of processing video data slows down due to the various requirements and complexity of MASS, however, there is no guarantee for safety as well as smooth service support. Trying to solve this issue, this study conducted research on the minimization of computation volumes for video data and the increased speed of data processing to detect maritime objects. Unlike previous studies that used the Hough transform algorithm to find the horizon and secure the areas of interest for the concerned objects, the present study proposed a new method of optimizing a binarization algorithm and finding areas whose locations were similar to actual objects in order to improve the speed. A maritime object detection system was materialized based on deep learning CNN to demonstrate the usefulness of the proposed method and assess the performance of the algorithm. The proposed algorithm performed at a speed that was 4 times faster than the old method while keeping the detection accuracy of the old method.

• International Journal of Highway Engineering
• /
• v.10 no.3
• /
• pp.19-31
• /
• 2008

Video image processing systems(VIPS) offer numerous benefits to transportation models and applications, due to their ability to monitor traffic in real time. VIPS based on wide-area detection, i.e., multi-lane surveillance algorithm provide traffic parameters with single camera such as flow and velocity, as well as occupancy and density. However, most current commercial VIPS utilize a tripwire detection algorithm that examines image intensity changes in the detection regions to indicate vehicle presence and passage, i.e., they do not identify individual vehicles as unique targets. If VIPS are developed to track individual vehicles and thus trace vehicle trajectories, many existing transportation models will benefit from more detailed information of individual vehicles. Furthermore, additional information obtained from the vehicle trajectories will improve incident detection by identifying lane change maneuvers and acceleration/deceleration patterns. The objective of this research was to relate traffic safety to VIPS tracking and this paper has developed a computer vision system of monitoring individual vehicle trajectories based on image processing, and offer the detailed information, for example, volumes, speed, and occupancy rate as well as traffic information via tripwire image detectors. Also the developed system has been verified by comparing with commercial VIP detectors.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2015.05a
• /
• pp.947-950
• /
• 2015

In the cause of car accidents in Korea, drowsy driving has shown that it is larger fctors than drunk driving. Therefore, in order to prevent drowsy driving accidents, drowsiness detection and warning system for drivers has recently become a very important issue. Furthermore, Many researches have been published that measuring alpha wave of EEG signals is the effective way in order to be aware of drowsiness of drivers. In this study, we have developed EEG measuring device that applies a signal processing algorithm using the LabView program for detecting drowsiness. According to results of drowsiness inducement experiments for small test subjects, it was able to detect the pattern of EEG, which means drowsy state based on the changing of power spectrum, counterpart of alpha wave. After all, Comparing to the results of drowsiness pattern between commercial equipments and developed device, we could confirm acquiring similar pattern to drowsiness pattern. With this results, the driver's drowsiness prevention system expect that it will be able to contribute to lowering the death rate caused by drowsy driving accidents.