• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.565-569
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• 2007

To find optimal path is killer application in the telematics system. The shortest path of conventional system, however, isn't always optimal path. That is, the path with minimum travelling time could be defined as optimal path in the road networks. There are techniques and algorithms for finding optimal path. Hierarchical path algorithm categorizes road networks into major layer and minor layer so that the performance of operational time increases. The path searched is accurate as much as optimal path. At above 2 system, a method to allocate minor roads to major road region influences the performance extremely. This paper proposes methods to determine search space for selecting major roads in the hierarchical path algorithm. In addition, methods which apply the proposed methods to hierarchical route algorithm is presented.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.69-70
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• 2017

As the industry develops, the size of buildings and ships are getting bigger and more complicated. In such a complex space, emergency evacuation systems are required because of the possibility of casualties when an accident situation occurs. However, because present systems are composed of basic devices, such as alarms, emergency exit signs, and announcement regarding the situation and inform only the least information to evacuees, evacuees are not able to judge objectively. To solve these problems, various evacuation algorithms have been proposed. However, these studies aim to search evacuation routes based on specific risk factors or to model the effects of bottlenecks in evacuation situations. Therefore, there is a limit to apply to real systems. Therefore, we propose algorithms to search the optimal evacuation route considering various risk factors such as fire and bottleneck in evacuation situations and to be applicable in actual situation in this paper. Performance evaluation using computer simulations showed that the proposed scheme is effective.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.792-795
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• 2012

애드 혹 네트워크(MANET: Mobile Ad hoc NETworks)는 기본적인 내부구조(infrastructure) 없이 노드들만으로 네트워크 망을 구성한다. 경로 탐색 정책으로 리액티브(reactive) 방식과 프로액티브(proactive) 방식이 있는데, 전통적으로 리액티브 방식의 성능이 더 좋은 것으로 평가된다. 그리고 두가지 방식의 장점을 취합한 하이브리드(hybrid) 방식의 클러스터 토폴로지(cluster topology) 도입에 관한 연구가 이루어지고 있다. 그 중, HCR(Hybrid Cluster Routing)이 제안되었는데, 이는 프로액티브 방식에 보다 중심을 둔 기법이다. HCR 은 리액티브 방식 경로 탐색 방법인 플라딩(flooding)의 탐색 지역을 한정된 범위로 제한할 수 있으나, 프로액티브 방식의 전체 네트워크 구성 정보 유지에 따른 막대한 오버헤드를 발생한다. 본 논문에서는 이러한 오버헤드를 줄이기 위해, 클러스터 내부 경로 탐색 기법인 MICF(Maginot path based Intra Cluster Flooding)를 제안한다. MICF 는 HCR 을 개선한 FSRS(First Search and Reverse Setting) 기반의 기법으로서, 클러스터 내부의 마지노 패스(maginot path)를 기준으로 경로 탐색 지역을 제한한다. MICF 는 게이트웨이(gateway) 간 최단 거리가 항상 클러스터 헤드(cluster head)를 중점으로 원의 내각 지역에 존재함을 바탕으로 하며, 최단 경로의 보장과 플라딩 지역 제한을 동시에 만족한다. 실험 결과, MICF 는 FSRS 기반의 기존 클러스터 내부 플라딩 방식보다 총 에너지의 7.79%만큼 더 에너지를 보존하였다. 결론적으로, MICF 역시 기존의 방식보다 에너지를 더 효율적으로 사용할 수 있으며, 마지노패스 설정과 이를 기반으로 한 제어 과정에 추가적인 오버헤드가 발생하지 않는다. 그리고 플라딩 면적이 작을수록 오버헤드가 줄어들게 됨을 알 수 있다.

• Journal of the Society of Naval Architects of Korea
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• v.59 no.6
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• pp.385-392
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• 2022

Naval ship could face a variety of threats in isolated seas. In particular, fires and flooding are defined as disasters that are very likely to cause irreparable damage to ships. These disasters have a very high risk of personal injury as well. Therefore, when a disaster occurs, it must be quickly suppressed, but if there are people in the disaster area, the protection of life must be given priority. In order to quickly evacuate the ship crew in case of a disaster, we would like to propose a plan to quickly explore the evacuation route even in urgent situations. Using commercial escape simulation software, we obtain the data for deep neural network learning with simulations according to aisle characteristics and the properties and number of evacuation person. Using the obtained data, the passage prediction model is trained with a deep learning, and the passage time is predicted through the learned model. Construct a numerical map of a naval ship and construct a distance matrix of the vessel using predicted passage time data. The distance matrix configured in one of the path search algorithms, the Dijkstra algorithm, is applied to explore the evacuation path of naval ship.

• Convergence Security Journal
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• v.12 no.1
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• pp.3-8
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• 2012

MANET composed wireless nodes without fixed infrastructure provides high flexibility, but it has weak disadvantage to various attack. It has big weakness to DDoS attack because every node perform packet forwarding especially. In this paper, packet transmission information control technique is proposed to reduce damage of DDoS attack in MANET and search location of attacker when DDoS attacks occur. Hierarchical structure using gateway node is adopted for protect a target of attack in this study. Gateway node in cluster is included like destination nodes surely when source nodes route path to destination nodes and it protects destination nodes. We confirmed efficiency by comparing proposed method in this study with CUSUM and measured the quantity consumed memory of cluster head to evaluate efficiency of information control using to location tracing.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.6
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• pp.153-158
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• 2016

Broadcasting in multi-hop wireless sensor networks is a basic operation that supports many applications such as route search, setting up addresses and sending messages from the sink to sensor nodes. The broadcasting using flooding causes problems that can be mentioned as a broadcasting storm such as redundancy, contention and collision. A variety of broadcasting schemes using wireless sensor networks have been proposed to achieve superior performance rather than simple flooding scheme. Broadcasting algorithms in wireless sensor networks can be classified into six subcategories: flooding scheme, probabilistic scheme, counter-based scheme, distance-based scheme, location-based schemes, and neighbor knowledge-based scheme. This study analyzes a simple flooding scheme, probabilistic scheme, counter-based scheme, distance-based scheme, and neighbor knowledge-based scheme, and compares the performance and efficiency of each scheme through network simulation.

• The KIPS Transactions:PartC
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• v.17C no.4
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• pp.371-378
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• 2010

In this paper, we propose a new energy-efficient routing algorithm for sensor networks that selects a least energy consuming path among the paths formed by node with highest remaining energy and provides long network lifetime and uniform energy consumption by nodes. The pair distribution of the energy consumption over all the possible routes to the base station is one of the design objectives. Also, an alternate route search mechanism is proposed to cope with the situation in which no routing information is available due to lack of remaining energy of the neighboring nodes. Simulation results show that our algorithm extends the network lifetime and enhances the network reliability by maintaining relatively uniform remaining energy distribution among sensor nodes.

• Journal of Korean Society of Transportation
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• v.24 no.1 s.87
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• pp.97-108
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• 2006

At present, Korean many cities depend on subjective judgements of experts to estimate the number of bus-stops and inter-stop space. To get reliable results by using more objective procedure, we search for old studies and models, but they don't concern alighting demands and a random demand distributions. Our study recognize and overcome these limitation. We devide the demand into boarding and alighting demands, and define the model that can estimate flexibly optimal number of bus-stop and inter-stop space on each segment by the demand distribution. Also we apply this new model to a simple example route having various demand distributions As a result, the number of bus-stop on each segment can be estimate flexibly in proportion to boarding or alighting demand by using this model.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.4
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• pp.361-365
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• 2002

오랜 세월동안 위대한 이동수단을 만들어내고자 하는 인간의 꿈은 오늘날 눈부신 각종 운송기구를 만들어 내는 결실을 얻고 있다. 자동차 네비게이션 시스템도 그러한 결실중의 한 예라고 할 수 있을 것이다. 지능적으로 판단하고 정보를 처리할 수 있는 자동차 네비게이션 시스템을 부착함으로써 한 단계 발전한 운송수단으로 진화할 수 있을 것이다. 이러한 자동차 네비게이션 시스템의 단점이라면 한정된 리소스만으로 여러 가지 작업을 수행해야만 하는 어려움이다. 그래서 네비게이션 시스템의 주요 작업중의 하나인 경로를 추출하는 경로추출(Route Planning) 작업은 한정된 리소스에서도 최적의 경로를 찾을 수 있는 지능적인 방법이어야만 한다. 이러한 경로를 추출하는 작업을 하는데 기존에 일반적으로 쓰였던 두 가지 방법에는 Dijkstra s algorithm과 A*algorithm이 있다. 이 두 방법은 최적의 경로를 찾아낸다는 점은 있지만 경로를 찾기 위해서 알고리즘의 특성상 각각, 넓은 영역에 대하여 탐색작업을 해야 하고 또한 수행시간이 많이 걸린다는 단점과 또한 경로를 계산하기 위해서 Heuristic function을 추가적인 정보로 계산을 해야 한다는 단점이 있다. 본 논문에서는 적은 탐색 영역을 가지면서 또한 최적의 경로를 추출하는데 드는 수행시간은 작으며 나아가 동적인 교통환경에서도 최적의 경로를 추출할 수 있는 최적 경로 추출방법을 강화학습의 일종인 Q- Learning을 이용하여 구현해 보고자 한다.

• Journal of Korean Society of Transportation
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• v.20 no.2
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• pp.71-79
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• 2002

This paper presents an effective alternative K-paths calculation method based on a Evolution Program (EP). We developed efficient genetic operators for path calculation. A major problem of the existing approach(similarities among the paths) can be resolved using EP's. The performance of the suggested method is evaluated and compared with the k-th shortest path for the virtual road network model by computer simulation. The results of computational experiments of the suggested method are found to be satisfactory in terms of the dispersion of alternatives.