• The Journal of Bigdata
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• v.8 no.2
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• pp.45-54
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• 2023

Seoul's public bicycle system, 'Ttareungyi,' introduced in 2015, has achieved an annual ridership of 40 million in 2022. Similarly, electric scooters, a type of personal mobility device, surpassed one million riders in 2020 due to various sharing platforms. However, the major roadways for these new transportation, bicycle lanes, are notably insufficient compared to other forms of transport. Hence, this study proposes an optimal location selection method for bicycle lanes in Seoul to prevent accidents and enhance bicycle ride safety. The location selection process prioritizes road safety concerning bicycle accident risk. Using regression models, high-risk areas for bicycle accidents are identified. Cluster analysis categorizes these areas into six clusters, each suggesting suitable types of bicycle lanes based on cluster-specific characteristics. We hope that this study will contribute to the improvement of Seoul's transportation environment, including the expansion of dedicated bicycle lanes and lanes for personal mobility devices.

• The Korean Journal of Financial Management
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• v.18 no.1
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• pp.107-128
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• 2001

금융기관이 직면하는 시장위험관리와 관련된 연구는 이자율과 주식가격 변동위험, 또는 환율과 이자율 변동위험만을 고려한 자산배분모델이므로 그 모형의 정교성에도 불구하고 국제금융기관의 시장위험관리 모형으로 이용하기에는 부족한 점이 있다. 시장위험인 VAR를 측정하는 방법 중 포트폴리오 VAR 측정방법인 델타-노말 방법을 응용하여 금융기관이 시장위험을 종합적으로 관리하는 한편, 기대수익을 최대화시키는 자산-부채의 최적배분에 대한 모형을 유도할 수 있다. 본 논문은 포트폴리오 접근법을 이용하여 금융기관의 시장위험을 종합적으로 관리할 수 있는 모형을 개발하는 동시에 미국, 일본, 영국, 독일의 주요 금융자산의 가격변동자료를 바탕으로 실증적 분석을 시도하였다. 이론적 모형과 관련하여 국제금융기관이 시장위험을 통제하는 한편 목표수익을 달성하는데 필요한 $m_1$ 종류의 국내자산과 부채의 규모, $m_2$ 종류의 외화자산과 부채의 규모를 동시적으로 결정할 수 있는 모델을 개발하였다. 이 모형은 금융기관의 위험포지션과 목표수익이 변동함에 따라 재구성되어야 할 국내외 자산과 부채의 포트폴리오에 대한 종류와 규모를 구체적으로 파악할 수 있게 한다. 실증분석을 위해 미국에 본점을 두고 미국, 일본, 영국, 독일에서 영업활동을 하는 국제금융기관이 16개의 국내외 금융자산을 이용 가능한 것으로 가정하였다. 1995년 1월부터 1999년 6월까지 이들 금융자산의 월별자료와 각 국 통화의 대 U.S. 달러 환율을 이용하여 목표이익 10,000천 달러를 실현하는 한편 이자율과 환율 위험을 최소화시키는 자산, 부채의 적정구성에 관한 결과를 제시하였다.구의 성과로는 특정 투자자 집단이 주가의 움직임에 따라 매매를 하는 수동적 전략의 의미보다는 적극적으로 주가를 움직이는 주체로서 외국인투자자와 일부 기관투자자의 존재를 확인할 수 있었다는 점이며, 주가 움직임에 따른 개인투자자와 일부 기관 투자자의 수동적 매매 스타일과 기관투자자 사이의 투자스타일의 이질성을 통계적으로 확인할 수 있었다는 데에 있다.남아 각국과 우리나라간에는 주가변동에 시차가 없는 것으로 나타났다. 그러나 각국간 표준시차 및 거래소 거래시간을 고려하면 미국, 영국, 독일의 경우에도 그 시차는 1일이내이거나 거의 시차가 없는 것으로 판단된다. 발견되어 선물의 선도효과가 지배적임을 발견하였다.적 일정하게 하는 소비행동을 목표로 삼고 소비와 투자에 대한 의사결정을 내리고 있음이 실증분석을 통하여 밝혀졌다. 투자자들은 무위험 자산과 위험성 자산을 동시에 고려하여 포트폴리오를 구성하는 투자활동을 행동에 옮기고 있다.서, Loser포트폴리오를 매수보유하는 반전거래전략이 Winner포트폴리오를 매수보유하는 계속거래전략보다 적합한 전략임을 알 수 있었다. 다섯째, Loser포트폴리오와 Winner포트폴리오를 각각 투자대상종목으로써 매수보유한 반전거래전략과 계속거래 전략에 대한 유용성을 비교검증한 Loser포트폴리오와 Winner포트폴리오 각각의 1개월 평균초과수익률에 의하면, 반전거래전략의 Loser포트폴리오가 계속거래전략의 Winner포트폴리오보다 약 5배정도의 높은 1개월 평균초과수익률을 실현하였고, 반전거래전략의 유용성을 충분히 발휘하기 위하여 장단기의 투자기간을 설정할 경우에 6개월에서 36개월로 이동함에 따라 6개월부터 24개월까지는 초과수익률이 상승하지만,

• Korean Journal of Culture and Social Issue
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• v.18 no.3
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• pp.415-433
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• 2012

This study was conducted in order to explain the effect of driving behavior determinants such as drivers' personality and attitude that may induce risky driving behavior and to develop a valid method for discriminating risky drivers using the determinants. In the results of surveying 534 adult drivers, 5 driving behavior determinants (avoidance of problems, benefit/stimulus seeking, interpersonal anxiety, interpersonal anger, and aggression) were found to have a statistically significant effect on drivers' various risky driving behaviors. Using these factors, drivers were grouped according to risk levels (normal drivers, unintentionally risky drivers, and intentionally risky drivers). This result suggests that drivers' dangerous behavior level can be predicted using psychological factors such as their personality and attitude. Accordingly, if the driving behavior determinant model and the base score system used in this study are improved through further research, they are expected to be useful in predicting drivers' recklessness in advance, identifying problems, and providing differentiated safe driving education services based on the results.

• Journal of Navigation and Port Research
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• v.39 no.3
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• pp.199-207
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• 2015

In ports of Korea, the marine traffic flow is congested due to a large number of vessels coming in and going out. In order to improve the safety and efficiency of these vessels, South Korea is operating with a Vessel Traffic Service System, which is monitoring its waters for 24 hours. However despite these efforts of the VTS (Vessel Traffic Service) officers, collisions are occurring continuously, the risk situation is analyzed that occurs once in about 20 minutes, the risk may be greater. It investigated to reduce these accidents by providing a safety standard for collision danger in a timely manner. Thus, this study has developed a risk prediction module to predict risk in advance. This module can avoid collision risk to adjust the speed and course of ship using a risk evaluation model based on ship operator's risk perspective. Using this module, the ship operators and VTS officers can easily be identified risks in complex traffic situations, so they can take an appropriate action against danger in near future including course and speed change. To verify the effectiveness of this module, this paper predicted the risk of each encounter situation and confirmed to be capable of identifying a risk changes in specific course and speed changes at Busan coastal water.

• International Journal of Highway Engineering
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• v.11 no.3
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• pp.61-74
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• 2009

Traffic accident risk index Computation model's development apply traffic level of significance about area of road user group, road and street network area, population group etc.. through numerical formula or model by countermeasure to reduce the occurrence rate of traffic accidents. Is real condition that is taking advantage of risk by tangent section through estimation model and by method to choose improvement way to intersection from outside the country, and is utilizing being applied in part business in domestic. However, question is brought in the accuracy being utilizing changing some to take external model in domestic real condition than individual development of model. Therefore, selection intersection estimation element through traffic accidents occurrence present condition, geometry structure, control way, traffic volume, turning traffic volume etc. in 96 intersections in this research, and select final variable through correlation analysis of abstracted estimation elements. Developed intersection design model taking advantage of signal type, numeric of lane, intersection type, analysis of variance techniques through ANOVA analysis of three variables of intersection form with selected variable lastly, in signal crossing through three class intersection, distinction variable choice risk in model, no-signal crossing risk distinction analysis model and so on develop.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.2
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• pp.148-153
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• 2003

A DoS(Denial of Service) attack appears in the form of the intrusion attempt and Syn Flooding attack is a typical example. The Syn Flooding attack takes advantage of the weak point of 3-way handshake between the end-points of TCP which is the connection-oriented transmission service and has the reliability This paper proposes a NIIP(Network based Intelligent Intrusion Prevention) model. This model captures and analyzes the packet informations for the detection of Syn Flooding attack. Using the result of analysis of decision module, the decision module, which utilizes FCM(Fuzzy Cognitive Maps), measures the degree of danger of the DoS and trains the response module to deal with attacks. This model is a network based intelligent intrusion prevention model that reduces or prevents the danger of Syn Flooding attack.

• Journal of Navigation and Port Research
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• v.28 no.1
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• pp.9-16
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• 2004

The purpose of this study is to examine the algorithm of ship collision avoidance system and to improve its performance. The study on the algorithm of ship collision avoidance system have been carried out by many researchers. We can divide the study according to the adopted theory into two category such as 'collision risk calculation method' and 'risk area method'. It is not so difficult to find heir merit and demerit in the respective method. This study suggested newly modified model, which can overcome a limit in the two method. The suggested model is based on collision risk calculation method and suggests how to solve the threshold value problem, that is, one of the unsolved issues in collision risk calculation method. To solve that problem this study proposed new system under which the users can select appropriate threshold value according to environments such as traffic situations and weathers conditions. Simulation results of new model is schematized using 'risk area method'to examine the relationships between the two method. In addition, in case of 'collision risk method', when TCPA and DCPA are used to determine collision risk, a problem happens, that is, two ships become too close in their stem area, therefore, partial function of 'risk area method'is adopted to solve the problem in suggested model.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.11
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• pp.411-418
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• 2022

Incorrectly loaded containers can easily knock down by strong winds. Container collapse accidents can lead to material damage and paralysis of the port system. In this paper, We propose a deep learning-based container loading state and accident risk detection technique. Using Darknet-based YOLO, the container load status identifies in real-time through corner casting on the top and bottom of the container, and the risk of accidents notifies the manager. We present criteria for classifying container alignment states and select efficient learning algorithms based on inference speed, classification accuracy, detection accuracy, and FPS in real embedded devices in the same environment. The study found that YOLOv4 had a weaker inference speed and performance of FPS than YOLOv3, but showed strong performance in classification accuracy and detection accuracy.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.315-315
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• 2023

급격한 기후변화에 기인하여 전 세계적으로 거듭되는 돌발홍수로 인한 피해가 급격히 증가하고 있는 실정이다. 우리나라에서도 최근 중소 규모의 하천 뿐 아니라 도시 생활하천, 도심지 저지대에서 갑작스런 홍수와 침수로 인해 많은 인명과 재산 피해를 경험하고 있다. 이런 문제를 인식하여 최근 정부차원에서 다양한 센서와 인공지능에 기반하는 많은 인프라 및 연구 투자가 이루어지고 있지만, 높은 설치 및 운영 비용과 우리나라의 복잡한 하천 환경 특성으로 인해 소하천이나 도심지 저지대에서는 그 효율성을 제대로 발휘하지 못하고 있다. 따라서 본 논문에서는 주변환경의 변화에 강인한 복합 센서단말을 통해, 하천 정보(유량, 유속, 수위 등)을 실시간 측정하고, 해당지역의 특성을 고려한 하천 또는 저지대의 위험도를 스스로 판단할 수 있는 기술을 제안한다. 또한, 본 논문에서는 제안한 저비용 초소형의 단말 장치로 지점의 하천 정보를 실시간 측정하여 IoT망을 통해 3차원 하천 디지털트윈 모델로 전달하여, 유속과 수위를 그대로 재연함으로써, 하천 침수 위험 감시의 효율성을 검증하였다. 3차원 DEM(Digital Elevation Models) 데이터와 실제 하천을 관측한 데이터를 이용한 디지털트윈 검증 결과, 데이터 전송 지연시간을 감안하여 3초 이내에 하천의 수위와 유속이 3차원 모델에 반영되는 것을 확인하였다. 이 결과로부터 열악한 환경에서도 실시간 하천 상황을 원거리에서 모니터링 할 수 있으며, 강우와 유출에 따른 하천 홍수 메카니즘을 새롭게 시뮬레이션할 수 있는 방법론을 제시할 수 있을 것으로 기대한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.5
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• pp.892-898
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• 2012

Rough classification methods are used to improving the recognition speed in many character recognition problems. In this case, some irreversible result can occur by an error in rough classification. Methods for duplicating each model in several classes are used in order to reduce this risk. But the errors by rough classfication can not be completely ruled out by these methods. In this paper, an recognition method is proposed to increase speed that matches models selectively without any increase in error. This method constructs a model graph using similarity between models. Then a search process begins from a particular point in the model graph. In this process, matching of unnecessary models are reduced that are not similar to the input pattern. In this paper, the proposed method is applied to the recognition problem of handwriting numbers and upper/lower cases of English alphabets. In the experiments, the proposed method was compared with the basic method that matches all models with input pattern. As a result, the same recognition rate, which has shown as the basic method, was obtained by controlling the out-degree of the model graph and the number of maintaining candidates during the search process thereby being increased the recognition speed to 2.45 times.