• Korean Journal of Cognitive Science
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• v.11 no.1
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• pp.67-80
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• 2000

Distributed cooperative information system is the one that has various knowledge sources as well as problem solving capabilities to get information in a distributed and heterogeneous data environment. In a distributed cooperative information system. a control mechanism to facilitate the available information is very important. and usually the role of the control mechanism determines the behavior of the total system In this research. we proposed a model of the distributed cooperative information system which is based on the multi-agent paradigm. We also implemented a test system to show l its feasibility. The proposed system makes the knowledge sources into agents and a special agent called 'facilitator' controls the cooperation between the knowledge agents The facilitator uses the knowledge granularity level to determine the sequence of the activation of the agents. In other words. the knowledge source with simple but fast processing mechanism activates first while more sophisticated but slow knowledge sources are activated late. In an environment in which we have several knowledge sources for the same topic. the proposed system will simulate the focusing mechanism of human cognitive process.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.523-525
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• 1998

웨이브렛 변환을 사용한 영상은 인간의 시각체계(HVS)에 적적한 양자화 및 부호화를 위한 계층적 구조를 제공한다. 인간의 시각이 민감하게 감지하는 부분을 더 정확하게 부호화 하고, 그렇지 못한 부분에서는 적절한 정보의 손실을 허용하는 원리를 이상적으로 적용할 수 있다. 이런 웨이브렛 변환영상의 통계적 특성을 이용한 제로트리 부호화 기법은 중요한 영상계수를 선별하여 영상전체에서 의미있는 계수를 순서대로 부호화 함으로써 매입 파일(Embeded File)을 생성한다. 본 연구에서는 제로트리 부호화 과정중 불확실 구간(Uncertainty Interval)에서 발생하는 오차(Error)를 줄이기 위하여 LBG(Line Bute Gray) 알고리즘을 도입하여 최적의 오차 코드북을 생성한 후 복원시 사용함으로써 결과의 향상을 보였다. 오차교정(Error Correction)은 EZW(Embeded Zerotee Wavelet) 부호화 과정에서 손실되기 쉬운 고주파수 밴드에 적용하여 복원시 영상의 자세한(detail) 성분을 살리는데 효과적이다. JPEG과 같은 DCT 기반 영상압축 결과에 비하여 화질에서 더 좋은 성능을 보일뿐아니라, DCT 특유의 블록화 현상도 제거되었다.

• Resources Recycling
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• v.6 no.2
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• pp.70-77
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• 1997

The paper revlcws [he kinds and thcir physical and chemical characleristics of varlnus plastics. The nccds for ~ccycling of used plastics are emphasized and problems and challcngcs ilssoclatcd with treatments of such used plastics are described It aLra makes various suggestions to meet and solve these problems.

• The KIPS Transactions:PartB
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• v.15B no.2
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• pp.137-146
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• 2008

Fault localization plays a significant role in enormous distributed system because it can identify root cause of observed faults automatically, supporting self-managing which remains an open topic in managing and controlling complex distributed systems to improve system reliability. Although many Artificial Intelligent techniques have been introduced in support of fault localization in recent research especially in increasing complex ubiquitous environment, the provided functions such as diagnosis and prediction are limited. In this paper, we propose fault localization for self-managing in performance evaluation in order to improve system reliability via learning and analyzing real-time streams of system performance events. We use probabilistic reasoning functions based on the basic Bayes' rule to provide effective mechanism for managing and evaluating system performance parameters automatically, and hence the system reliability is improved. Moreover, due to large number of considered factors in diverse and complex fault reasoning domains, we develop an efficient method which extracts relevant parameters having high relationships with observing problems and ranks them orderly. The selected node ordering lists will be used in network modeling, and hence improving learning efficiency. Using the approach enables us to diagnose the most probable causal factor with responsibility for the underlying performance problems and predict system situation to avoid potential abnormities via posting treatments or pretreatments respectively. The experimental application of system performance analysis by using the proposed approach and various estimations on efficiency and accuracy show that the availability of the proposed approach in performance evaluation domain is optimistic.

• Journal of Korea Multimedia Society
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• v.7 no.3
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• pp.427-437
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• 2004

Shape comparison between 3D models is essential for shape recognition, retrieval, classification, etc. In this paper, we propose a method for comparing 3D shapes, which is invariant under translation, rotation and scaling of models and is robust to non-uniformly distributed and incomplete data sets. first, a modal model is constructed from input data using vibration modes and then shape similarity is evaluated with modal strain energy. The proposed method provides global-to-local ordering of shape deformation using vibration modes ordered by frequency Thus, we evaluated similarity in terms of global properties of shape without being affected localised shape features using ordered shape representation and modal strain one energy.

• Convergence Security Journal
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• v.18 no.5_1
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• pp.33-43
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• 2018

In this paper, we want to solve the problems that users want to search the progress of attack, continuity of attack, association between attackers and victims, blocking priority and countermeasures by using visualization interface with multi-rotational axis and radial axis structure. It is possible to effectively monitor and track security events by arranging a time series event based on a multi-rotational axis structured by an event generation order, a subject of an event, an event type, and an emission axis, which is an objective time indicating progress of individual events. The proposed interface is a practical visualization interface that can apply attack blocking and defense measures by providing the progress and progress of the whole attack, the details and continuity of individual attacks, and the relationship between attacker and victim in one screen.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.7
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• pp.22-28
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• 2009

As multiprocessors have been widely adopted in embedded systems, task computation energy consumption should be minimized with several low power techniques supported by the multiprocessors. This paper proposes an energy-aware task scheduling algorithm that adopts both dynamic voltage scaling and power shutdown in multiprocessor environments. Considering the timing and energy overhead of power shutdown, the proposed algorithm performs an iterative task assignment and task ordering for multiprocessor systems. In this case, the iterative priority-based task scheduling is adopted to obtain the best solution with the minimized total energy consumption. Total energy consumption is calculated by considering a linear programming model and threshold time of power shutdown. By analyzing experimental results for standard task graphs based on real applications, the resource and timing limitations were analyzed to maximize energy savings. Considering the experimental results, the proposed energy-aware task scheduling provided meaningful performance enhancements over the existing priority-based task scheduling approaches.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.12
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• pp.2132-2139
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• 2006

A parallel program including a nested parallelism has a complex execution aspects and tasks are executed concurrently. This concurrency is a main cause raising most of errors. In this paper, a new method for checking concurrency between two tasks is proposed. The existing techniques for checking the concurrency have their limits to represent a global structure. A new labeling technique that appropriate for image visualization is proposed. To show the global structure by imaging of execution aspects through region partition on 2D plane. On the basis of it, each of the tasks that can distinguish the ordered relation create an independent image. Image information generated by the result simplifies semantic analysis of the related task, and provides an outline of a global execution aspects structure of the program to user effectively.

• The KIPS Transactions:PartD
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• v.14D no.5
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• pp.459-470
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• 2007

A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Query processing for such a data stream should also be continuous and rapid, which requires strict time and space constraints. In most DSMS(Data Stream Management System), the selection predicates of continuous queries are grouped or indexed to guarantee these constraints. This paper proposes a new scheme tailed an ASC(Attribute Selection Construct) that collectively evaluates selection predicates containing the same attribute in multiple continuous queries. An ASC contains valuable information, such as attribute usage status, partially pre calculated matching results and selectivity statistics for its multiple selection predicates. The processing order of those ASC's that are corresponding to the attributes of a base data stream can significantly influence the overall performance of multiple query evaluation. Consequently, a method of establishing an efficient evaluation order of multiple ASC's is also proposed. Finally, the performance of the proposed method is analyzed by a series of experiments to identify its various characteristics.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.39-47
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• 2024

Time series analysis is widely employed by many organizations to solve business problems, as it extracts various information and insights from chronologically ordered data. Among its applications, measuring time series similarity is a step to identify time series with similar patterns, which is very important in time series analysis applications such as time series search and clustering. In this study, we propose an efficient method for measuring time series similarity that focuses on anomalies rather than the entire series. In this regard, we validate the proposed method by measuring and analyzing the rank correlation between the similarity measure for the set of subsets extracted by anomaly detection and the similarity measure for the whole time series. Experimental results, especially with stock time series data and an anomaly proportion of 10%, demonstrate a Spearman's rank correlation coefficient of up to 0.9. In conclusion, the proposed method can significantly reduce computation cost of measuring time series similarity, while providing reliable time series search and clustering results.