• Current Optics and Photonics
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• v.8 no.4
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• pp.382-390
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• 2024

Laser propagation through atmospheric disturbances is vital for applications such as laser optical communication, satellite laser ranging (SLR), laser guide stars (LGS) for adaptive optics (AO), and laser energy transmission systems. Beam degradation, including energy loss and pointing errors caused by atmospheric turbulence, requires thorough numerical analysis. This paper investigates the impact of laser beam parameters on ground-to-space laser propagation up to an altitude of 100 km using vertical atmospheric disturbance profiles from the Geochang SLR Observatory in South Korea. The analysis is confined to 100 km since sodium LGS forms at this altitude, and beyond this point, beam propagation can be considered free space due to the absence of optical disturbances. Focusing on a 100-watt class laser, this study examines parameters such as laser wavelengths, beam size (diameter), beam jitter, and beam quality (M²). Findings reveal that jitter, with an influence exceeding 70%, is the most critical parameter for long-exposure radius and pointing error. Conversely, M², with an influence over 45%, is most significant for short-exposure radius and scintillation.

• Journal of KIISE:Databases
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• v.32 no.4
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• pp.372-382
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• 2005

Aggregation such as computing an average value of data measured in each sensor commonly occurs in many applications of sensor networks. Since sensor networks consist of low-cost nodes with limited battery power, reducing energy consumption must be considered in order to achieve a long network lifetime. Reducing the amount of messages exchanged is the most important for saving energy. Earlier work has demonstrated the effectiveness of in-network data aggregation and data filtering for minimizing the amount of messages in sensor networks. In this paper, we propose an adaptive error adjustment scheme that is simpler, more effective and efficient than previous work. The proposed scheme is based on self-adjustment in each sensor node. We show through various experiments that our scheme reduces the network traffic significantly, and performs better than existing methods.

• Journal of KIISE:Software and Applications
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• v.37 no.3
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• pp.185-193
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• 2010

With the emergence of ubiquitous computing era, various models for providing personalized service have been proposed, and, especially, several recommendation service schemes have been proposed to give tailored services to users proactively. However, the previous recommendation service schemes utilize a wide range of data without and filtering and consider the limited context-aware information to predict user preferences so that they are not adequate to provide personalized service to users. In this paper, we propose an adaptive recommendation service scheme which proactively provides suitable services based on the current context. We use accumulated interaction contexts (IC) between users and devices for predicting the user's preferences and recommend adaptive service based on the current context by utilizing clustering and collaborative filtering. The clustering algorithm improves efficiency of the recommendation service by focusing and analyzing the data that is collected from the locations nearby the users. Collaborative filtering guarantees an accurate recommendation, even when the data is insufficient. Finally, we evaluate the performance and the reliability of the proposed scheme by simulations.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.43 no.5 s.311
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• pp.1-9
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• 2006

Because most applications of vision-based object tracking demonstrate satisfactory operations only under very constrained environments that have simplifying assumptions or specific visual attributes, these approaches can't track target objects for the highly variable, unstructured, and dynamic environments like a traffic scene. An adaptive fusion framework is essential that takes advantage of the richness of visual information such as color, appearance shape and so on, especially at cluttered and dynamically changing scenes with partial occlusion[1]. This paper develops a particle filter based adaptive fusion framework and improves the robustness and adaptation of this framework by adding a new distinctive visual attribute, an image feature descriptor using SIFT (Scale Invariant Feature Transform)[2] and adding an automatic teaming scheme of the SIFT feature library according to viewpoint, illumination, and background change. The proposed algorithm is applied to track various traffic objects like vehicles, pedestrians, and bikes in a driver assistance system as an important component of the Intelligent Transportation System.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.673-676
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• 2009

In this paper, we describe CAMAR Companoin, a context-aware mobile AR system that provides a user-adaptive assistance with an augmented picture according to the user's context in smart space. It recognizes physical objects and tracks the movement of those objects with a camera embodied to a mobile device. CAMAR Companion observes a mobile user's context, which is sensed by various kinds of sensors in environments, and infers user preference for the content in the situation. It recommends multimedia content relevant to the user's context. It overlays selected content over associated physical objects and enables the user to experience the content in a user-centric manner. Furthermore, we have developed the prototype to illustrate how our system could be used for a mobile user's well-being care applications in smart home environments. In this application, we found that our system could perceive a user preference even though a user's context is changed dynamically, and then adapt the multimedia content with respect to the user's context effectively. As such, the proposed user-adaptive system has the potential to play an important role in developing customized user interfaces in mobile devices.

• Journal of KIISE:Software and Applications
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• v.41 no.7
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• pp.457-468
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• 2014

Recently, several approaches are proposed to support developing Self-Adaptive System. However, they do not provide the way to accept interaction between requirements and architecture. It makes difficult to judge the impact of changing requirements, handle quickly, and understand adaptation process for stakeholder. To overcome above problems, this paper suggests the interweaving method for providing traceability based on the relationship between requirements and architecture. This traceability allows tracing the impact of changing requirements, and it provides the rationale of architectural decision for advanced degree of understanding. Example shows the usefulness through developing process and changing process on Smart Grid domain.

• Journal of Korea Multimedia Society
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• v.13 no.7
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• pp.991-999
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• 2010

In this paper, we propose a fast motion estimation algorithm for video encoding. Conventional fast motion estimation algorithms have a serious problem of low prediction quality in some frames. However, full search based fast algorithms have low computational reduction ratio. In the paper, we propose an algorithm that significantly reduces unnecessary computations, while keeping prediction quality almost similar to that of the full search. The proposed algorithm uses distribution probability of motion vectors and adaptive search patterns and block matching criteria. By taking different search patterns and error criteria of block matching according to distribution probability of motion vectors, we can reduces only unnecessary computations efficiently. Our algorithm takes only 20~30% in computational amount and has decreased prediction quality about 0~0.02dB compared with the fast full search of the H.264 reference software. Our algorithm will be useful to real-time video coding applications using MPEG-2 or MPEG-4 AVC standards.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.11B
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• pp.1038-1049
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• 2003

Exactly measuring traffic load is the basis for efficient traffic engineering. However, precise traffic measurement involves inspecting every packet traversing a lint resulting in significant overhead on routers with high-speed links. Sampling techniques are proposed as an alternative way to reduce the measurement overhead. But, since sampling inevitably accompany with error, there should be a way to control, or at least limit, the error for traffic engineering applications to work correctly. In this paper, we address the problem of bounding sampling error within a pre-specified tolerance level. We derive a relationship between the number of samples, the accuracy of estimation and the squared coefficient of variation of packet size distribution. Based on this relationship, we propose an adaptive random sampling technique that determines the minimum sampling probability adaptively according to traffic dynamics. Using real network traffic traces, we show that the proposed adaptive random sampling technique indeed produces the desired accuracy, while also yielding significant reduction in the amount of traffic samples.

• Journal of KIISE:Software and Applications
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• v.31 no.10
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• pp.1345-1355
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• 2004

In a sender-initiated load redistribution algorithm, a sender(overloaded processor) continues to send unnecessary request messages for load transfer until a receiver(underloaded processor) is found while the system load is heavy. In a receiver-initiated load redistribution algorithm, a receiver continues to send unnecessary request messages for load acquisition until a sender is found while the system load is light. Therefore, it yields many problems such as low CPU utilization and system throughput because of inefficient inter-processor communications in this environment. This paper presents an approach based on genetic algorithm(GA) for adaptive load sharing in distributed systems. In this scheme, the processors to which the requests are sent off are determined by the proposed GA to decrease unnecessary request messages.

• Journal of KIISE:Software and Applications
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• v.35 no.1
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• pp.41-49
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• 2008

Fingerprint classification is useful to reduce the matching time of a huge fingerprint identification system by categorizing fingerprints into predefined classes according to their global features. Although global features are distributed diversly because of the uniqueness of a fingerprint, previous fingerprint classification methods extract global features non-adaptively from the fixed region for every fingerprint. We propose an novel method that extracts features adaptively for each fingerprint in order to classify various fingerprints effectively. It extracts ridge directional values as feature vectors from the region after searching the feature region by calculating variations of ridge directions, and classifies them using support vector machines. Experimental results with NIST4 database show that we have achieved a classification accuracy of 90.3% for the five-class problem and 93.7% for the four-class problem, and proved the validity of the proposed adaptive method by comparison with non-adaptively extracted feature vectors.