• Journal of Electrical Engineering and Technology
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• 제12권2호
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• pp.612-620
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• 2017

Relay coordination in power system is a complex problem and so far, meta-heuristic algorithms and other methods as an alternative approach may not properly deal with large scale relay coordination due to their huge time consuming computation. In some cases the relay coordination could be unachievable. As the urgency for a proper approach is essential, in this paper an innovative and simple relay coordination method is introduced that is able to be applied on optimization algorithms for relay protection coordination. The objective function equation of operating time of relays are divided into two separate functions with less constraints. As the analytical results show here, this equivalent method has a remarkable speed with high accuracy to coordinate directional relays. Two distribution systems including directional overcurrent relays are studied in DigSILENT software and the collected data are examined in MATLAB. The relay settings of this method are compared with particle swarm optimization and genetic algorithm. The analytical results show the correctness of this mathematical and practical approach. This fast coordination method has a proper velocity of convergence with low iteration that can be used in large scale systems in practice and also to provide a feasible solution for protection coordination in smart grids as online or offline protection coordination.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권11호
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• pp.2737-2753
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• 2013

In this study, we propose a novel salient object detection strategy based on regional contrast and relative spatial compactness. Our algorithm consists of four basic steps. First, we learn color names offline using the probabilistic latent semantic analysis (PLSA) model to find the mapping between basic color names and pixel values. The color names can be used for image segmentation and region description. Second, image pixels are assigned to special color names according to their values, forming different color clusters. The saliency measure for every cluster is evaluated by its spatial compactness relative to other clusters rather than by the intra variance of the cluster alone. Third, every cluster is divided into local regions that are described with color name descriptors. The regional contrast is evaluated by computing the color distance between different regions in the entire image. Last, the final saliency map is constructed by incorporating the color cluster's spatial compactness measure and the corresponding regional contrast. Experiments show that our algorithm outperforms several existing salient object detection methods with higher precision and better recall rates when evaluated using public datasets.

• Journal of information and communication convergence engineering
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• 제12권3호
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• pp.168-172
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• 2014

In this paper, we propose an algorithm that can efficiently detect and monitor multiple ships in real-time. The proposed algorithm uses morphological operations and edge information for detecting and tracking ships. We used smoothing filter with a $3{\times}3$ Gaussian window and luminance component instead of RGB components in the captured image. Additionally, we applied Sobel operator for edge detection and a threshold for binary images. Finally, object labeling with connectivity and morphological operation with open and erosion were used for ship detection. Compared with conventional methods, the proposed method is meant to be used mainly in coastal surveillance systems and monitoring systems of harbors. A system based on this method was tested for both stationary and non-stationary backgrounds, and the results of the detection and tracking rates were more than 97% on average. Thousands of image frames and 20 different video sequences in both online and offline modes were tested, and an overall detection rate of 97.6% was achieved.

• 한국컴퓨터정보학회논문지
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• 제12권6호
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• pp.49-57
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• 2007

P2P 분산 시스템은 네트워크로 연결된 다양한 컴퓨팅 환경 하에 존재하는 유휴 컴퓨팅 자원을 활용함으로써 다양한 연구가 활발히 진행되고 있다. 이는 복수로 존재하는 검색 대상 파일들 가운데 다운로드 시간이 가장 짧은 피어를 대상으로 P2P 통신이 이루어지는 것이 일반적인 방법이다. 여기에 P2P 검색 알고리즘이 복수로 존재하는 검색 대상 파일들 가운데 다운로드 시간이 가장 짧은 피어를 선택하는 기준에 따라 실제 다운로드 시간을 결정하는 가장 중요한 요인이다. 그러나 네트워크 연결성이 약하기 때문에 자원 제공 피어의 오프라인 상태로 전환 될 수 있고, 이때 주로 자원 재전송의 방법을 선택하게 된다. 본 연구에서는 자원 재전송 요구 발생시 성능 개선을 위한 역 추적 자원 검색 알고리즘을 제안한다.

• Journal of Computing Science and Engineering
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• 제5권4호
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• pp.346-353
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• 2011

With the advent of ubiquitous computing, a massive amount of trajectory data has been published and shared in many websites. This type of computing also provides motivation for online mining of trajectory data, to fit user-specific preferences or context (e.g., time of the day). While many trajectory clustering algorithms have been proposed, they have typically focused on offline mining and do not consider the restrictions of the underlying road network and selection conditions representing user contexts. In clear contrast, we study an efficient clustering algorithm for Boolean + Clustering queries using a pre-materialized and summarized data structure. Our experimental results demonstrate the efficiency and effectiveness of our proposed method using real-life trajectory data.

• 제어로봇시스템학회논문지
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• 제11권7호
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• pp.565-571
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• 2005

We present a novel speed estimation of a Permanent Magnet Synchronous Motor(PMSM) based on Support Vector Regression(SVR). The proposed method can estimate wide speed range, including 0.33Hz with full load, accurately in the steady and transient states where motor parameters variations are known without parameter estimator. Moreover, the method does not need offline training previously but is trained on-line. The training starts with the PMSM operation simultaneously and estimates the speed in real time. The experimental results shows the validity and the usefulness of the proposed algorithm for the 0.4Kw PMSM DSP(TMS320VC33) drive system.

• ETRI Journal
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• 제41권6호
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• pp.739-749
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• 2019

Security and accuracy are two issues in the localization of wireless sensor networks (WSNs) that are difficult to balance in hostile indoor environments. Massive numbers of malicious positioning requests may cause the functional failure of an entire WSN. To eliminate the misjudgments caused by malicious nodes, we propose a compressive-sensing-based multiregional secure localization (CSMR_SL) algorithm to reduce the impact of malicious users on secure positioning by considering the resource-constrained nature of WSNs. In CSMR_SL, a multiregion offline mechanism is introduced to identify malicious nodes and a preprocessing procedure is adopted to weight and balance the contributions of anchor nodes. Simulation results show that CSMR_SL may significantly improve robustness against attacks and reduce the influence of indoor environments while maintaining sufficient accuracy levels.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2012년도 제46차 하계학술발표논문집 20권2호
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• pp.335-338
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• 2012

교통카드는 1996년 충전방식의 선불카드가 처음 사용되었고, 후불방식의 교통카드는 1998년 6월부터 도입되어 함께 사용되었다. 교통카드 사용할 수 있기 위해서는 사용자의 사용여부 및 각종 신상정보의 변경에 따라 결제방식이 변경되는 것에 대해 카드거래 승인시스템에 적절한 반영이 필요하다. 이를 위해 기존 서울교통시스템에서는 메모리 주소를 이용한 카드거래승인시스템을 이용하고 있으며 본 연구에서는 임베디드 교통카드단말기에서 사용 가능한 오프라인 카드거래 승인 알고리즘을 개발하는 것을 목표로 한다. 본 논문에서는 카드 사용정보를 압축 저장하는 방식을 제안하고 있으며, 사용자 할인정보는 한 카드당 2bit의 공간을 차지하도록 설계 했다. 또한 검색알고리즘으로 이진탐색을 사용하여 기존에 비해 검색 속도가 향상 되었다.

• Journal of Information Processing Systems
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• 제11권4호
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• pp.643-654
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• 2015

Face recognition under controlled settings, such as limited viewpoint and illumination change, can achieve good performance nowadays. However, real world application for face recognition is still challenging. In this paper, we propose using the combination of Affine Scale Invariant Feature Transform (SIFT) and Probabilistic Similarity for face recognition under a large viewpoint change. Affine SIFT is an extension of SIFT algorithm to detect affine invariant local descriptors. Affine SIFT generates a series of different viewpoints using affine transformation. In this way, it allows for a viewpoint difference between the gallery face and probe face. However, the human face is not planar as it contains significant 3D depth. Affine SIFT does not work well for significant change in pose. To complement this, we combined it with probabilistic similarity, which gets the log likelihood between the probe and gallery face based on sum of squared difference (SSD) distribution in an offline learning process. Our experiment results show that our framework achieves impressive better recognition accuracy than other algorithms compared on the FERET database.

• 한국벤처창업학회:학술대회논문집
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• 한국벤처창업학회 2019년도 추계학술대회
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• pp.135-141
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• 2019

최근 대표적 글로벌 유통기업인 미국의 아마존과 중국의 알리바바가 전 세계적으로 가장 큰 시장점유가 있으며 두 기업의 국내진입 시 국내 유통산업에 큰 영향을 미칠 것으로 예상한다. 두 기업은 온라인 기업이 오프라인 기업을 흡수 합병함으로써 새로운 가치를 창출해내는 O2O (Online to Offline) 추세가 국제적으로 진행되고 있다. 아마존과 알리바바와 같은 글로벌 유통업체들은 일본, 인도와 같은 타 국가로의 세계 진출을 적극적으로 하는 추세이다. 본 연구에서는 아마존, 알리바바와 같은 글로벌 유통업체가 세계 진출의 일환으로 국내 유통시장 진입 시, 노출된 글로벌 경쟁 속에서 국내 유통기업들의 사업전망을 예측해보고, 해당 예측에 기반하여 기업 차원의 전략적 대응방안 및 정부 차원의 정책 지원방안을 마련하는 데 그 목적이 있다. 시장 현황분석을 기반으로 하여, 미래 시장예측 방법으로써 무작위로 추출된 난수(Random Number)를 이용하여 원하는 방정식의 값을 확률적으로 구하기 위한 알고리즘(Algorithm) 및 시뮬레이션(Simulation)의 방법인 몬테카를로(Monte Carlo, MC) 방법론을 사용하여 국내 유통시장의 변화를 예측하여 본 연구를 진행하였다.