• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.1
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• pp.76-87
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• 2005

In this paper, we propose a new profile, ROHC(RObust Header Compression) profile 7, for SCTP with ROHC for applying robust header compression SCTP, which is a transport layer protocol. The proposed new profile 7 adds a new field of 1 or 2 byte size on the existing SCTP packet header, which can make the SCTP stream to be diveded into acknowledgement stream and data stream. In addition, the classification of the stream can be used for recovering fault context. Consequently, in the case of using proposed ROHC-SCTP, it is possible to reduce the SCTP header overhead rate and also can be saved bandwidth.

• The KIPS Transactions:PartD
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• v.14D no.7
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• pp.733-742
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• 2007

A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Due to this reason, memory usage for data stream analysis should be confined finitely although new data elements are continuously generated in a data stream. To satisfy this requirement, data stream processing sacrifices the correctness of its analysis result by allowing some errors. The old distribution statistics are diminished by a predefined decay rate as time goes by, so that the effect of the obsolete information on the current result of clustering can be eliminated without maintaining any data element physically. This paper proposes a grid based clustering algorithm for a data stream. Given a set of initial grid cells, the dense range of a grid cell is recursively partitioned into a smaller cell based on the distribution statistics of data elements by a top down manner until the smallest cell, called a unit cell, is identified. Since only the distribution statistics of data elements are maintained by dynamically partitioned grid cells, the clusters of a data stream can be effectively found without maintaining the data elements physically. Furthermore, the memory usage of the proposed algorithm is adjusted adaptively to the size of confined memory space by flexibly resizing the size of a unit cell. As a result, the confined memory space can be fully utilized to generate the result of clustering as accurately as possible. The proposed algorithm is analyzed by a series of experiments to identify its various characteristics

• The Korean Journal of Ecology
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• v.23 no.2
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• pp.89-100
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• 2000

On community data. sampled in regular intervals on a long-term basis. artificial neural networks were implemented to extract information on characterizing patterns of community changes. The Adaptive Resonance Theory and Kohonen Network were both utilized in learning benthic macroinvertebrate communities in the Soktae Stream of the Suyong River collected monthly for three years. Initially, by regarding each monthly collection as a separate sample unit, communities were grouped into similar patterns after training with the networks. Subsequently, changes in communities in a sequence of samplings (e.g., two-month, four-month, etc.) were given as input to the networks. After training, it was possible to recognize new data set in line with the sampling procedure. Through the comparative study on benthic macroinvertebrates with these learning processes, patterns of community changes in chironomids diverged while those of the total benthic macro-invertebrates tended to be more stable.

• The Korean Journal of Ecology
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• v.21 no.5_1
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• pp.409-418
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• 1998

A molel study was conducted to determine the optimal sample size for the analysis of the aquatic insect community in a stream reach of the Pangtae Creek, Kangwon-do in October 1995 and may 1996. The results showed that the required minimum sample size varied and depended on the purposes of the community analysis. Acoording to the Species: Area Curve method, at least 16 Surber samplings ($30{\times}30cm$) were required in a stream reach in each spring and fall survey. The species diversity index did not vary significantly as the sample size increased. Based on the coefficient of variation analysis, the minimum sample sizes of 10 were required in order to compare seasonal differences of the community in the study area. Considering the static community structure of aquatic insects, including both species numbers and individual numbers of aquatic insects, 11 and 7 samplings were optimal sizes for the fall and spring survey, respectively. We concluded that 12 Surber samplings from 3 riffle-pool sequences (4 samplings at each riffle-pool sequence) would be required in a stream reach (length 1 km) to obtain reliable as well as cost efficient data. Our model showed that the optimal sample size should be determined by interactions between minimum sample size, the degree of data reliability, and cost efficiency.

• Journal of KIISE:Databases
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• v.35 no.6
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• pp.459-468
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• 2008

A data stream which is a massive unbounded sequence of data elements continuously generated at a rapid rate. Many recent research activities for emerging applications often need to deal with the data stream. Such applications can be web click monitoring, sensor data processing, network traffic analysis. telephone records and multi-media data. For this. data processing over a data stream are not performed on the stored data but performed the newly updated data with pre-registered queries, and then return a result immediately or periodically. Recently, many studies are focused on dealing with a data stream more than a stored data set. Especially. there are many researches to optimize continuous queries in order to perform them efficiently. This paper proposes a query optimization algorithm to manage continuous query which has multiple join operators(Multi-way join) over data streams. It is called by an Extended Greedy query optimization based on a greedy algorithm. It defines a join cost by a required operation to compute a join and an operation to process a result and then stores all information for computing join cost and join cost in the statistics catalog. To overcome a weak point of greedy algorithm which has poor performance, the algorithm selects the set of operators with a small lay, instead of operator with the smallest cost. The set is influenced the accuracy and execution time of the algorithm and can be controlled adaptively by two user-defined values. Experiment results illustrate the performance of the EGA algorithm in various stream environments.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.109-112
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• 2002

In this paper, we propose the efficient digital watermarking scheme to transmit effectively the compressed medical image that embedded with watermarking data in mobile Internet access channel. The wireless channel error based on multiple access interference (MAI) is closely related to the length of spreading sequence in CDMA system. Also, the fixed length coded medical image with watermark bit stream can be classified by significance of source image. In the simulation, we compare the peak signal to noise ratio (PSNR) performance when the watermarked image with a simple symbol and when the watermarked image with a text file is transmitted using variable length of spreading sequences in case of limited length of spread sequence.

• Journal of the Korea Society of Computer and Information
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• v.15 no.9
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• pp.35-46
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• 2010

Sequential pattern mining is one of the essential data mining tasks, and it is widely used to analyze data generated in various application fields such as web-based applications, E-commerce, bioinformatics, and USN environments. Recently data generated in the application fields has been taking the form of continuous data streams rather than finite stored data sets. Considering the changes in the form of data, many researches have been actively performed to efficiently find sequential patterns over data streams. However, conventional researches focus on reducing processing time and memory usage in mining sequential patterns over a target data stream, so that a research on mining more interesting and useful sequential patterns that efficiently reflect the characteristics of the data stream has been attracting no attention. This paper proposes a mining method of sequential patterns over data streams with a gap constraint, which can help to find more interesting sequential patterns over the data streams. First, meanings of the gap for a sequential pattern and gap-constrained sequential patterns are defined, and subsequently a mining method for finding gap-constrained sequential patterns over a data stream is proposed.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.673-677
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• 2009

In this paper, a transmitting process of a sequence of holograms describing 3D moving objects over the communicating wireless-network system is presented. A sequence of holograms involves holograms is transformed into a bit stream data, and then it is transmitted over the wireless LAN and Bluetooth. It is shown that applying this technique, holographic data of 3D moving object is transmitted in high quality and a relatively good reconstruction of holographic images is performed.

• ETRI Journal
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• v.40 no.1
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• pp.111-121
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• 2018

To find the emerging patterns (EPs) in streaming transaction data, the streaming is first divided into some time windows containing a number of transactions. Itemsets are generated from transactions in each window, and then the emergence of itemsets is evaluated between two windows. In the tilted-time windows model (TTWM), it is assumed that people need support data with finer accuracy from the most recent windows, while accepting coarser accuracy from older windows. Therefore, a limited array's elements are used to maintain all support data in a way that condenses old windows by merging them inside one element. The capacity of elements that accommodates the windows inside is modeled using a particular number sequence. However, in a stream, as new data arrives, the current array updating mechanisms lead to many null elements in the array and cause data incompleteness and inaccuracy problems. Two models derived from TTWM, logarithmic TTWM and Fibonacci windows model, also inherit the same problems. This article proposes a novel push-front Fibonacci windows model as a solution, and experiments are conducted to demonstrate its superiority in finding more EPs compared to other models.

• Journal of Ecology and Environment
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• v.38 no.1
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• pp.95-107
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• 2015

Because an ecological community consists of diverse species that vary nonlinearly with environmental variability, its dynamics are complex and difficult to analyze. To investigate temporal variations of benthic macroinvertebrate community, we used the community data that were collected at the sampling site in Baenae Stream near Busan, Korea, which is a clean stream with minimum pollution, from July 2006 to July 2013. First, we used a self-organizing map (SOM) to heuristically derive the states that characterizes the biotic condition of the benthic macroinvertebrate communities in forms of time series data. Next, we applied the hidden Markov model (HMM) to fine-tune the states objectively and to obtain the transition probabilities between the states and the emission probabilities that show the connection of the states with observable events such as the number of species, the diversity measured by Shannon entropy, and the biological water quality index (BMWP). While the number of species apparently addressed the state of the community, the diversity reflected the state changes after the HMM training along with seasonal variations in cyclic manners. The BMWP showed clear characterization of events that correspond to the different states based on the emission probabilities. The environmental factors such as temperature and precipitation also indicated the seasonal and cyclic changes according to the HMM. Though the usage of the HMM alone can guarantee the convergence of the training or the precision of the derived states based on field data in this study, the derivation of the states by the SOM that followed the fine-tuning by the HMM well elucidated the states of the community and could serve as an alternative reference system to reveal the ecological structures in stream communities.