• Journal of Korea Multimedia Society
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• v.6 no.5
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• pp.797-804
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• 2003

Scene change detection algorithm, which is very important preprocessing technique for video indexing and retrieval and determines the performance of video database system, is being studied widely. In this paper, we propose a more effective abrupt scene change detection, which is robust to large motion, sudden change of light and successive abrupt shot transitions rapidly. And we also propose a new gradual scene change detection algorithm, which can detect dissolve, and fade in/out precisely. Furthermore, we also propose a representative frame extraction algorithm which performs content-based video summary by novel DCT DC image buffering technique and accumulative histogram intersection measure (AHIM).

• Journal of Korean Society on Water Environment
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• v.29 no.5
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• pp.691-702
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• 2013

This review paper summarizes the theory, application, and potential drawbacks of diffusive gradient in thin film (DGT) probe which is a widely used in-situ passive sampling technique for monitoring inorganic contaminants in aquatic environments. The DGT probe employs a series of layers including a filter membrane, a diffusive hydrogel, and an ionic exchange resin gel in a plastic unit. The filter side is exposed to an aquatic environment after which dissolved inorganic contaminants, such as heavy metals and nuclides, diffuse through the hydrogel and are accumulated in the resin gel. After retrieval, the contaminants in the resin gel are extracted by strong acid or base and the concentrations are determined by analytical instruments. Then aqueous concentrations of the inorganic contaminants can be estimated from a mathematical equation. The DGT has also been used to monitor nutrients, such as ${PO_4}^{3-}$, in lakes, streams, and estuaries, which might be helpful in assessing eutrophic potential in aquatic environments. DGT is a robust in-situ passive sampling techniques for investigating bioavailability, toxicity, and speciation of inorganic contaminants in aquatic environments, and can be an effective monitoring tool for risk assessment.

• Journal of Internet Computing and Services
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• v.1 no.1
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• pp.1-14
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• 2000

Visual information retrieval and image databases are very important applications of spatial access methods. The quaries for these applications are visual and based not on exact match but on dubjective similarity. The individual aperations of spatial access methods are much more expensive than those of conventional one-dimensional access methods. Also, because the visual queries are much more complex than textual queries, an efficient processing technique for visual queries is one of the critical requirements in the development of large and scalable image databases. Therefore, efficient translation and execution for the complex visual queries are not less important than those of textual databases. In this paper, we introduce our cognitive and topological studies that are required to process subjective visual queries effectively. Then, we propose an efficient translation and execution techniques for similarity based visual queries by conducting these related studies.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.4
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• pp.367-374
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• 1999

This paper suggests an integrated method based on concept ascension and attribute reduction for efficient induction of decision rules from a large database. We study an automatic scheme to generate concept trees by a clustering technique, a method for generalizing databases by the concept ascension technique, an optimal reduction method by means of attributes reduction using the sibmificance of attributes, and an efficient way of reduction of attribute values applying the discernible matrix and functions. The method can be used for the decision making tasks such as an investment planning or price evaluation, the construction of knowledge bases for diagnosis of defects or medical diagnosis, data analysis such as marketing or experimental data, information retrieval for high level inquiries, and so on.

• Journal of the Korean association of regional geographers
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• v.3 no.2
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• pp.105-120
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• 1997

One of the most important issues of object-oriented geographical database system is to develop an indexing technique which enables more efficient I/O processing within aggregation hierarchy or inheritance hierarchy. Up to present, several indexing schemes have been developed for this purpose. However, they have separately focused on aggregation hierarchy or inheritance hierarchy of object-oriented data model. A recent research is proposing a nested-inherited index which combines these two hierarchies simultaneously. However, this new index has some weak points. It has high storage costs related to its use of auxiliary index. Also, it cannot clearly represent the inheritance relationship among classes within its index structure. To solve these problems, this thesis proposes a pointer-chain index. Using pointer chain directory, this index composes a hierarchy-typed chain to show the hierarchical relationship among classes within inheritance hierarchy. By doing these, it could fetch the OID list of objects to be retrieved more easily than before. In addition, the pointer chain directory structure could accurately recognize target cases and subclasses and deal with "select-all" typed query without collection of schema semantic information. Also, it could avoid the redundant data storing, which usually happens in the process of using auxiliary index. This study evaluates the performance of pointer chain indexing technique by way of simulation method to compare nested-inherited index. According to this simulation, the pointer chain index is proved to be more efficient with regard to storage cost than nested-inherited index. Especially in terms of retrieval operation, it shows efficient performance to that of nested-inherited index.

• Journal of Broadcast Engineering
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• v.5 no.1
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• pp.102-112
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• 2000

The previous video structuring techniques are mainly limited to shot or shot group level. However, the shot level structure couldn't provide semantics within a video. So, researches on high level structuring are going on for getting over the drawbacks of shot level structure, recently. To overcome the drawbacks of shot level structure, we propose video scene segmentation technique based on color and motion features. For considering various color distribution, each shot is divided into sub-shots based on color feature. A key frame is extracted from each sub-shot. The motion feature in a shot is extracted from MPEG-1 video's motion vector. Moreover adaptive weights based on motion's property in search range are applied to color and motion features. The experiment results of proposed technique show the excellence in view of the over-segmentation and the reflection of semantics, comparing with those of previous techniques. The proposed technique decomposes video into meaningful hierarchical structure and provides video browsing or retrieval based on scene.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.1
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• pp.68-79
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• 2011

This paper focuses on the opinion information extraction and analysis system through Web mining that is based on statistics collected from Web contents. That is, users' opinion information which is scattered across several websites can be automatically analyzed and extracted. The system provides the opinion information search service that enables users to search for real-time positive and negative opinions and check their statistics. Also, users can do real-time search and monitoring about other opinion information by putting keywords in the system. Proposing technique proved that the actual performance is excellent by comparison experiment with other techniques. Performance evaluation of function extracting positive/negative opinion information, the performance evaluation applying dynamic window technique and tokenizer technique for multilingual information retrieval, and the performance evaluation of technique extracting exact multilingual phonetic translation are carried out. The experiment with typical movie review sentence and Wikipedia experiment data as object as that applying example is carried out and the result is analyzed.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.933-948
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• 2023

Atmospheric aerosols not only have adverse effects on human health but also exert direct and indirect impacts on the climate system. Consequently, it is imperative to comprehend the characteristics and spatiotemporal distribution of aerosols. Numerous research endeavors have been undertaken to monitor aerosols, predominantly through the retrieval of aerosol optical depth (AOD) via satellite-based observations. Nonetheless, this approach primarily relies on a look-up table-based inversion algorithm, characterized by computationally intensive operations and associated uncertainties. In this study, a novel high-resolution AOD direct retrieval algorithm, leveraging machine learning, was developed using top-of-atmosphere reflectance data derived from the Geostationary Ocean Color Imager-II (GOCI-II), in conjunction with their differences from the past 30-day minimum reflectance, and meteorological variables from numerical models. The Light Gradient Boosting Machine (LGBM) technique was harnessed, and the resultant estimates underwent rigorous validation encompassing random, temporal, and spatial N-fold cross-validation (CV) using ground-based observation data from Aerosol Robotic Network (AERONET) AOD. The three CV results consistently demonstrated robust performance, yielding R²=0.70-0.80, RMSE=0.08-0.09, and within the expected error (EE) of 75.2-85.1%. The Shapley Additive exPlanations(SHAP) analysis confirmed the substantial influence of reflectance-related variables on AOD estimation. A comprehensive examination of the spatiotemporal distribution of AOD in Seoul and Ulsan revealed that the developed LGBM model yielded results that are in close concordance with AERONET AOD over time, thereby confirming its suitability for AOD retrieval at high spatiotemporal resolution (i.e., hourly, 250 m). Furthermore, upon comparing data coverage, it was ascertained that the LGBM model enhanced data retrieval frequency by approximately 8.8% in comparison to the GOCI-II L2 AOD products, ameliorating issues associated with excessive masking over very illuminated surfaces that are often encountered in physics-based AOD retrieval processes.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.2
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• pp.164-168
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• 2002

In this article, we present the novel approach of avoiding temporal insufficiency of data blocks, jitter, which occurs due to the commencement of new session. We propose to make the sufficient amount of data blocks available on memory such that the ongoing session can survive the cycle extension. This technique is called ″pre-buffering″. We examine two different approaches in pre-buffering: (i) loads all required data blocks prior to starting playback and (ii) incrementally accumulates the data blocks in each cycle. We develop an elaborate model to determine the appropriate amount of data blocks necessary to survive the cycle extension and to compute startup latency involved in loading these data blocks. The simulation result shows that limiting the disk bandwidth utilization to 60% can greatly improve the startup latency as well as the buffer requirement for individual streams.

• Proceedings of the Korean Society for Information Management Conference
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• 2002.08a
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• pp.247-252
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• 2002

본 연구에서는 하이퍼텍스트나 웹 문서의 검색에서 링크로 연결된 문서들이 주제적으로 서로 관련되어 있다는 것을 기반으로 하여 링크정보를 참조한 웹 문서 클러스터링 기법을 제안하였고 이것을 이용하여 검색된 결과를 질의 근접 순위화함으로써 웹 문서 검색의 성능을 향상시키는 방안을 연구하였다. 본 연구에서 사용된 웹 문서 집단은 웹(WWW)을 통하여 직접 수집하였으며 웹 문서가 다른 웹 문서를 링크하고 있을 때를 OutLink, 다른 웹 문서로부터 링크를 받고 있을 때를 InLink로 구분하였다. 실험결과 OutLink를 참조하여 클러스터링을 수행하는 기법과 InLink를 참조하여 클러스터링을 수행하는 기법 모두 검색 성능을 향상시켰다.