• The Transactions of the Korea Information Processing Society
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• v.6 no.8
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• pp.2271-2280
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• 1999

In this paper, a variable motion estimation scheme based on HBMA(Hierarchical Block Matching Algorithm) to improve the performance and to reduce heavy computational and transmission load, is presented. The proposed algorithm is composed of four steps. First, block activity for each block is defined using the edge information of differential image between two sequential images, and then average block activity of the present image is found by taking the mean of block activity. Secondly, camera pan compensation is carried out, according to the average activity of the image, in the hierarchical pyramid structure constructed by wavelet transform. Next, the LUT classifying each block into one among Moving, No Moving, Semi-Moving Block according to the block activity compensated camera pan is obtained. Finally, as varying the block size and adaptively selecting the initial search layer and the search range referring to LUT, the proposed variable HBMA can effectively carries out fast motion estimation in the hierarchical pyramid structure. The cost function needed above-mentioned each step is only the block activity defined by the edge information of the differential image in the sequential images.

• Korean Journal of Remote Sensing
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• v.16 no.3
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• pp.199-209
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• 2000

This study improves the feature tracking method (FTM) in estimating the ocean current vectors from the sequential AVHRR satellite imageries by adding the objective algorithm in defining the edges and boundaries of the oceanic eddies and fronts. It was implemented by using the Sobel operator. The Sobel operator has been proved to be in effective filter in detecting the edges of any object on the image. In estimating the current vectors on the edges defined by the Sobel operator, center coordinates of the Pattern and Search tiles need to be determined by the investigator. The objective feature tracking method combined with maximum cross correlation method (MCC) is turned out to be very efficient and fast, since it uses only parts of the image containing the objects instead of searching the entire image. In the validation with the in situ ADCP measurements of currents in the East Sea, the estimated current speed values are around 35% lower than and current directions are deviated by $34^{\circ}$ from ADCP current vectors. The results are regarded as improved ones compared to the previous investigators'.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.23 no.8
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• pp.556-561
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• 2011

In this study, the theoretical maximum power of a heat engine was investigated by sequential Carnot cycle model, for a low-grade heat source of about $100^{\circ}C$. In contrast to conventional approaches, the pattern search algorithm was employed to optimize the two design variables to maximize power. Variations of the maximum power and the optimum values of design variables were investigated for a wide range of UA(overall heat transfer conductance) change. The results show that maximizing heat source utilization does not always maximize power.

• Journal of Ship and Ocean Technology
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• v.9 no.3
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• pp.1-13
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• 2005

A design procedure for a ship with minimum total resistance has been developed using a numerical optimization method called SQP (Sequential Quadratic Programming) to search for optimized hull form and CFD(Computational Fluid Dynamics) technique. The friction resistance is estimated using the ITTC 1957 model-ship correlation line formula and the wave making resistance is evaluated using a potential-flow panel method based on Rankine sources with nonlinear free surface boundary conditions. The geometry of hull surface is represented and modified using B-spline surface patches during the optimization process. Using the Series 60 hull ($C_B$ =0.60) as a base hull, the optimization procedure is applied to obtain an optimal hull that produces the minimum total resistance for the given constraints. To verify the validity of the result, the original model and the optimized model obtained by the optimization process have been built and tested in a towing tank. It is shown that the optimal hull obtained around $13\%$ reduction in the total resistance and around $40\%$ reduction in the residual resistance at a speed tested compared with that of the original one, demonstrating that the present optimization tool can be effectively used for efficient hull form designs.

• Journal of Distribution Science
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• v.20 no.3
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• pp.33-43
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• 2022

Purpose: More and more marketers use social media platforms to create and spread information called Marketer Generated Content (MGC) to inform consumers of products. MGC often embeds product purchase links, thus directing consumers to online distribution channels for online purchases. This study examined the effect of social media MGC on consumers' willingness to buy online in the anchor of consumers' perspective to answer the question of "how social media generated content support corporate online distribution". Research design, data, and methodology: According to the means-end-chain theory, we introduce perceived value and continuous following intention as chain mediators to explain the mechanism of MGC influence on consumers' online purchase intention and consider product type to discuss boundary conditions. Two experiments were designed to test hypothesizes. Results and Conclusion: First, emotional MGC (vs. informational MGC) has lower (higher) perceived utility (hedonic) value. Second, perceived value has a significant mediate role in the effect of MGC on continuous following intention. Third, perceived value and continuous following intention significantly and sequentially mediated the effect of MGC on online purchase intention. Through the sequential mediations of perceived utility value and continuous following intention, Informational MGC of search products significantly increase online purchase intentions. Another parallel sequential mediation, including perceived hedonic, emotional MGC of experience products, partially enhanced online purchase intentions. Finally, this study gives implications for how corporates can use social media MGC to promote product sales online.

• Journal of KIISE:Databases
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• v.33 no.6
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• pp.600-619
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• 2006

According to recent technical advances on sensors and mobile devices, processing of data streams generated by the devices is becoming an important research issue. The data stream of real values obtained at continuous time points is called streaming time-series. Due to the unique features of streaming time-series that are different from those of traditional time-series, similarity matching problem on the streaming time-series should be solved in a new way. In this paper, we propose an efficient algorithm for streaming time- series matching problem that supports normalization transform. While the existing algorithms compare streaming time-series without any transform, the algorithm proposed in the paper compares them after they are normalization-transformed. The normalization transform is useful for finding time-series that have similar fluctuation trends even though they consist of distant element values. The major contributions of this paper are as follows. (1) By using a theorem presented in the context of subsequence matching that supports normalization transform[4], we propose a simple algorithm for solving the problem. (2) For improving search performance, we extend the simple algorithm to use $k\;({\geq}\;1)$ indexes. (3) For a given k, for achieving optimal search performance of the extended algorithm, we present an approximation method for choosing k window sizes to construct k indexes. (4) Based on the notion of continuity[8] on streaming time-series, we further extend our algorithm so that it can simultaneously obtain the search results for $m\;({\geq}\;1)$ time points from present $t_0$ to a time point $(t_0+m-1)$ in the near future by retrieving the index only once. (5) Through a series of experiments, we compare search performances of the algorithms proposed in this paper, and show their performance trends according to k and m values. To the best of our knowledge, since there has been no algorithm that solves the same problem presented in this paper, we compare search performances of our algorithms with the sequential scan algorithm. The experiment result showed that our algorithms outperformed the sequential scan algorithm by up to 13.2 times. The performances of our algorithms should be more improved, as k is increased.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.169-189
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• 2015

Detection of anomalous events from video streams is a challenging problem in many video surveillance applications. One such application that has received significant attention from the computer vision community is traffic video surveillance. In this paper, a Lossy Count based Sequential Temporal Pattern mining approach (LC-STP) is proposed for detecting spatio-temporal abnormal events (such as a traffic violation at junction) from sequences of video streams. The proposed approach relies mainly on spatial abstractions of each object, mining frequent temporal patterns in a sequence of video frames to form a regular temporal pattern. In order to detect each object in every frame, the input video is first pre-processed by applying Gaussian Mixture Models. After the detection of foreground objects, the tracking is carried out using block motion estimation by the three-step search method. The primitive events of the object are represented by assigning spatial and temporal symbols corresponding to their location and time information. These primitive events are analyzed to form a temporal pattern in a sequence of video frames, representing temporal relation between various object's primitive events. This is repeated for each window of sequences, and the support for temporal sequence is obtained based on LC-STP to discover regular patterns of normal events. Events deviating from these patterns are identified as anomalies. Unlike the traditional frequent item set mining methods, the proposed method generates maximal frequent patterns without candidate generation. Furthermore, experimental results show that the proposed method performs well and can detect video anomalies in real traffic video data.

• The KIPS Transactions:PartD
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• v.13D no.7 s.110
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• pp.1027-1038
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• 2006

There are many technical problems in the recommendation system based on very large database(VLDB). So, it is necessary to study the recommendation system' structure and the data-mining technique suitable for the large scale Internet shopping mail. Thus we design and implement the product recommendation system using k-means clustering algorithm and sequential pattern technique which can be used in large scale Internet shopping mall. This paper processes user information by batch processing, defines the various categories by hierarchical structure, and uses a sequential pattern mining technique for the search engine. For predictive modeling and experiment, we use the real data(user's interest and preference of given category) extracted from log file of the major Internet shopping mall in Korea during 30 days. And we define PRP(Predictive Recommend Precision), PRR(Predictive Recommend Recall), and PF1(Predictive Factor One-measure) for evaluation. In the result of experiments, the best recommendation time and the best learning time of our system are much as O(N) and the values of measures are very excellent.

• The KIPS Transactions:PartD
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• v.10D no.7
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• pp.1093-1102
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• 2003

As the use of internet is growing, the amount of information is increasing rapidly and XML that is a standard of the web data has the property of flexibility of data representation. Therefore electronic document systems based on web, such as EDMS (Electronic Document Management System), ebXML (e-business extensible Markup Language), have been adopting XML as the method for exchange and standard of documents. So research on the method which can manage and search structural XML documents in an effective wav is required. In this paper we propose the clustering method based on structural similarity among the many XML documents, using typical structures extracted from each document by sequential pattern mining in pre-clustering process. The proposed algorithm improves the accuracy of clustering by computing cost considering cluster cohesion and inter-cluster similarity.

• Journal of KIISE:Information Networking
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• v.32 no.4
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• pp.543-550
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• 2005

Demand for better services in the Internet has been increasing due to the rapid growth of the Internet, and hence next generation routers are required to perform intelligent packet classification. For a given classifier defining packet attributes or contents, packet classification is the process of identifying the highest priority rule to which a packet conforms. A notable characteristic of real classifiers is that a packet matches only a small number of distinct source-destination prefix pairs. Therefore, a lot of schemes have been proposed to filter rules based on source and destination prefix pairs. However, most of the schemes are based on sequential one-dimensional searches using trio which requires huge memory. In this paper, we proposea memory-efficient two-dimensional search scheme using source and destination prefix pairs. By constructing binary prefix tree, source prefix search and destination prefix search are simultaneously performed in a binary tree. Moreover, the proposed two-dimensional binary prefix tree does not include any empty internal nodes, and hence memory waste of previous trio-based structures is completely eliminated.