• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.7 no.11
• /
• pp.2737-2753
• /
• 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
• /
• v.12 no.3
• /
• pp.168-172
• /
• 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.

• Journal of the Korea Society of Computer and Information
• /
• v.12 no.6
• /
• pp.49-57
• /
• 2007

The P2P distributed systems are proceeded various studies lively to use the idleness computing resources under the network connected computing environments. It's a general mean to communication from the peer of the shortest downloaded time among same target files to be searched. The P2P search algorithms are very important primary factor to decide a real downloaded time in the criteria to select the peer of a shortest downloaded time. However the peer to give resources could be changed into offline status because the P2P distributed systems have very weakness connection. In these cases. we have a choice to retransmit resources mainly. In this study, we suggested an effective backtracking search algorithm to improve the performance about the request to retransmit the resource.

• Journal of Computing Science and Engineering
• /
• v.5 no.4
• /
• pp.346-353
• /
• 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.

• Journal of Institute of Control, Robotics and Systems
• /
• v.11 no.7
• /
• pp.565-571
• /
• 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
• /
• v.41 no.6
• /
• pp.739-749
• /
• 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.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2012.07a
• /
• pp.335-338
• /
• 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
• /
• v.11 no.4
• /
• pp.643-654
• /
• 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
• /
• 2019.11a
• /
• pp.135-141
• /
• 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.

• International Journal of Computer Science & Network Security
• /
• v.21 no.4
• /
• pp.272-276
• /
• 2021

Presently, Online / Offline Users are facing cyber attacks every day. These cyber attacks affect user's performance, resources and various daily activities. Due to this critical situation, attention must be given to prevent such users through cyber attacks. The objective of this research paper is to improve the IDS systems by using machine learning approach to develop a hybrid model which controls the cyber attacks. This Hybrid model uses the available KDD 1999 intrusion detection dataset. In first step, Hybrid Model performs feature optimization by reducing the unimportant features of the dataset through decision tree, support vector machine, genetic algorithm, particle swarm optimization and principal component analysis techniques. In second step, Hybrid Model will find out the minimum number of features to point out accurate detection of cyber attacks. This hybrid model was developed by using machine learning algorithms like PSO, GA and ELM, which trained the system with available data to perform the predictions. The Hybrid Model had an accuracy of 99.94%, which states that it may be highly useful to prevent the users from cyber attacks.