• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.18 no.12
• /
• pp.1-8
• /
• 2017

This paper presents the development of an adaptive cruise control (ACC) system, which is one of the typical advanced driver assist systems, for 4-wheel drive hybrid in-wheel electric vehicles. The front wheels of the vehicle are driven by a combustion engine, while its rear wheels are driven by in-wheel motors. This paper proposes an adaptive cruise control system which takes advantage of the unique driveline configuration presented herein, while the proposed power distribution algorithm guarantees its tracking performance and fuel efficiency at the same time. With the proposed algorithm, the vehicle is driven only by the engine in normal situations, while the in-wheel motors are used to distribute the power to the rear wheels if the tracking performance decreases. This paper also presents the modeling of the in-wheel motors, hybrid in-wheel driveline, and integrated ACC control system based on a commercial high-precision vehicle dynamics model. The simulation results obtained with the model are presented to confirm the performance of the proposed algorithm.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.18 no.10
• /
• pp.707-716
• /
• 2017

The advances in image display devices necessitate display images suitable for the user's purpose. The display devices should be able to provide object-based image information when a depthmap is required. In this paper, we represent the algorithm using a histogram-based image segmentation method for depthmap generation. In the conventional K-means clustering algorithm, the number of centroids is parameterized, so existing K-means algorithms cannot adaptively determine the number of clusters. Further, the problem of K-means algorithm tends to sink into the local minima, which causes over-segmentation. On the other hand, the proposed algorithm is adaptively able to select centroids and can stand on the basis of the histogram-based algorithm considering the amount of computational complexity. It is designed to show object-based results by preventing the existing algorithm from falling into the local minimum point. Finally, we remove the over-segmentation components through connected-component labeling algorithm. The results of proposed algorithm show object-based results and better segmentation results of 0.017 and 0.051, compared to the benchmark method in terms of Probabilistic Rand Index(PRI) and Segmentation Covering(SC), respectively.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.24 no.3
• /
• pp.491-498
• /
• 2014

Recently, cyber terror has been occurred frequently based on advanced persistent threat(APT) and it is very difficult to detect these attacks because of new malwares which cannot be detected by anti-virus softwares. This paper proposes and verifies the algorithms to detect the advanced persistent threat previously through real-time network monitoring and combinatorial analysis of big data log. In the future, APT attacks can be detected more easily by enhancing these algorithms and adapting big data platform.

• Journal of Digital Convergence
• /
• v.19 no.12
• /
• pp.331-337
• /
• 2021

The adaptation of smartfarm technology that converges ICT is increasing productivity and competitiveness in the agriculture. Technologies have been developed that enable environmental monitoring through various sensors and automatic control of the cultivation environment, and researches are underway to advance smartfarm technology using data generated from smartfarms. In this paper, an environmental control method to reduce the energy consumption of a smartfarm by using the environment and control data of the smartfarm is proposed. It was confirmed that energy consumption could be reduced compared to an independent environmental control method by creating an environmental prediction model using accumulated environmental data and selecting a control method to minimize energy consumption in a given situation by considering multiple environmental factors. In the future, research is needed to obtain higher energy efficiency through the advancement of the predictive model and the improvement of the complex control algorithms.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.19 no.1
• /
• pp.154-166
• /
• 2016

This study aims to contribute to better understanding the effects of the changes in the parameter values of GroundFilter algorithm(GF), which performs filtering process, and of GridsurfaceCreate algorithm(GC), which creates regular grid, provided in Fusion software on the accuracy of elevation of the final LiDAR-DEM products through comparative analysis. In order to test whether there are significant effects on the accuracy of the final LiDAR-DEM products due to the changes of GF(1, 3, 5, 7, 9) parameter levels and GC(1, 3, 5, 7, 9) parameter levels, two-way ANOVA is conducted based on residuals. The residuals are calculated using the differences between each sample plot's paired field-measured and DEM-derived elevation values given each individual GF and GC level. After that, Tukey HSD test is conducted as a post hoc test for grouping the levels. As a result of two-way ANOVA test, it is found that the change in the GF levels significantly affects the accuracy of LiDAR-DEM elevations(F-value : 27.340, p < 0.01), while the change in the GC levels does not significantly affect the accuracy of LiDAR-DEM elevations(F-value : 0.457). It is also found that the interaction effect between GF and GC levels is not likely to exist(F-value : 0.247). From the results of the Tukey HSD test in the GF levels, GF levels can be divided into two groups('7', '5', '9', '3' vs '1') by the differences of means of residuals. Given the current conditions, LiDAR-DEM can achieve the best accuracy when the level '7' and '3' are given as GF and GC level, respectively.

• Proceedings of the Korea Contents Association Conference
• /
• 2003.11a
• /
• pp.113-119
• /
• 2003

Both the highly developed computing environment and the rapid increase of the internet users enable the present web based cluster system accounting service to help many users access to numerous data at high speed. However, the information security of users and data is also as important as the convenience of the systematic environment. Especially, the significance of damage to the individuals and organizations resulted from the data outflow, hacking and malicious coding has risen up to one of the most essential problems in the internet service business. In this study, I suggest a more safe web based cluster system accounting service solution applying SEED, the Korean Telecommunications Technology Association (TTA) standard encryption algorithm.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2017.11a
• /
• pp.86-87
• /
• 2017

여객선의 안전은 인명의 안전과 직결되므로 이에 발맞추어 국제해사기구 (International Maritime Organization, IMO)는 빈번히 발생하는 선박의 전복 사고로 인한 인명 피해를 최소화하기 위하여 승객탈출분석 (Passenger Evacuation Analysis) 적용 지침을 꾸준히 개정 및 보완하고 있다. 승객탈출분석의 목적은 설계 단계에서의 탈출 설비의 배치 적합성 판단 및 배치된 탈출 설비를 이용하여 지침 상 규정하는 최대허용탈출시간 (Maximum allowable evacuation duration) 내에 탈출이 가능한 지를 분석하는 것이다. 본 논문에서는 승객탈출분석에 관한 지침의 개정 동향 및 최근 개정된 승객탈출분석 지침 (MSC.1/Circ.1533)의 개정 방향에 대하여 소개하고자 한다. 또한 탈출 해석 관점 한국해양과학기술원에서 개발 중인 인명대피안내시스템의 유효성을 검증하기 위하여 2016년 10월 수행 된 인명대피실험과 지침 상 규정하는 고도화 알고리즘 (Advanced algorithm) 기반 승객탈출 시뮬레이션 (Maritime EXODUS)의 비교 결과를 바탕으로 해당 지침 적용 시 고려사항에 대하여 고찰하고자 한다.

• Journal of IKEEE
• /
• v.2 no.1 s.2
• /
• pp.134-138
• /
• 1998

Because of the statistical fluctuations and the high 'time-variability' nature of the traffic, managing the resources of the network require highly dynamic techniques with minimal Intervention and reaction times, and adaptive and learning capabilities. The neural networks normalizes the ATM cell arrival rate and queue length and has the adaptive learning algorithm, and experimentally investigated the method to prevent the congestion generated in ATM networks.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2013.11a
• /
• pp.509-512
• /
• 2013

최근 모바일 기기의 고도화 및 위성 기술의 발달에 따라, 모바일 단말에서 개인 위치의 수집이 용이해지고 있고, 이에 따라 개인 위치 정보 기반의 다양한 서비스들이 주목 받고 있다. 수집된 위치 정보를 필요한 기준에 따라 적절하게 분석한다면 다양한 위치기반 서비스 및 개인용 스마트 기기 인터페이스 등을 위한 매우 유용한 정보로 활용 할 수 있다. 예를 들어 각 지역별 유동인구 또는 사람들이 밀집한 특정 지역이나, 시간대를 확인 하여 위치기반 서비스의 성능을 향상 시킬 수 있다. 또한 스마트 기기에서 사용자의 위치와 연관된 이동 방식을 인지하여 개인 사용자에게 필요한 인터페이스를 제공할 수 있다. 이 논문에서는 위치 정보 수집을 위한 툴에 대할 설명과, 약 1개월간 수집된 위치정보를 기반으로 분석 결과를 소개한다. 이 결과를 토대로 이동 방식 인식을 위한 알고리즘 개발 시 필요한 점들을 고찰한다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• fall
• /
• pp.164-167
• /
• 2021

최근 구글, 아마존, LOD 등을 중심으로 지식 그래프(Knowledge graph)와 같은 검색 고도화 연구가 활발히 수행되고 있다.그러나 대규모 지식 그래프 인덱싱 시스템에서 데이터가 어떻게 임베딩(embedding)되고, 딥러닝(deep learning) 되는지는 상대적으로 거의 연구가 되지 않고 있다. 이에 본 논문에서는 임베딩 모델에 대한 성능평가를 통해 데이터셋에 대해 어떤 모델이 가장 좋은 지식 임베딩 방법을 도출하는지 분석한다.