• Journal of IKEEE
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• v.22 no.3
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• pp.850-853
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• 2018

In this paper, we propose a decoding method using a balanced binary tree and a canonical Huffman tree for efficient decoding of Huffman codes. The balanced binary tree scheme reduces the number of searches by lowering the height of the tree and binary search. However, constructing a tree based on the value of the code instead of frequency of symbol is a drawback of the balanced binary tree. In order to overcome these drawbacks, a balanced binary tree is reconstructed according to the occurrence probability of symbols at each level of the tree and binary search is performed for each level. We minimize the number of searches using a canonical Huffman tree to find level of code to avoid searching sequentially from the top level to bottom level.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.9 no.1
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• pp.47-54
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• 1991

In this study, the accuracy of three dimensional location was improved by self calibration bundle method with additional parameter, which is to correct systematic error through detection and elimination of the gross error from updated reference variance for observation values in photogram-metry. In this study, with the result of comparing accuracy of each method, correcting systematic error is more effective after gross error detection and when observation values are contained more than two gross error the point with maximum correlation value is detected by masking effect of least square adjustment.

• Knowledge Management Research
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• v.22 no.3
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• pp.75-89
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• 2021

A knowledge map should be able to support the referential navigation of knowledge inquiries, i.e., cross- and sequential searches and queries on content relevance-based associated knowledge. This study performs a user-oriented test to verify which type of knowledge map, hierarchical or networked, exhibits superior performance in supporting knowledge inquiries required for problem solving. Both the effectiveness identified by the correct answer rate and the efficiency identified by the number of completion time and reference documents have been revealed superior performance in the networked knowledge map. This study's result can underpin the basic steps to develop more user-friendly and reasonable knowledge services.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.5_6
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• pp.268-278
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• 2004

In this paper, we describe our experience in the design and implementation of the SMART file system to handle multimedia workload. Our work has three design objectives: (ⅰ) efficient support for sequential workload, (ⅱ) avoiding disk fragmentation, (ⅲ) logical unit based file access. To achieve these three objectives, we develop a file system where a file consists of linked list of Data Unit Group. Instead of tree like structure of the legacy Unix file system, we use single level file structure. Our file system can also access the file based upon the logical unit which can be video frame or audio samples. Data Unit Group is a group of logical data units which is allocated continuous disk blocks. At the beginning of each Data Unit Group, there exists an index array. Each index points to the beginning of logical data units, e.g. frames in the Data Unit Group. This index array enables the random access and sequencial access of semantic data units. SMART file system is elaborately tailored to effectively support multimedia workload. We perform physical experiments and compare the performance of SMART file system with EXT2 file system and SGI XFS file system. In this experiment, SMART file system exhibits superior performance under streaming workload.

• Journal of Korean Society of Transportation
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• v.21 no.6
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• pp.89-99
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• 2003

Considering a path represented by a sequence of link numbers in a network, the vine is differentiated from the loop in a sense that any link number can be appeared in the path only once, while more than once in the loop. The vine provides a proper idea how to account for complicated travel patterns such as U-turn and P-turn witnessed nearby intersections in urban roads. This paper proposes a new algorithm in which the vine travel pattern can be considered for finding K number of sequential paths. The main idea of this paper is achieved by replacing the node label of the existing Yen's algorithm by the link label technique. The case studies show that the algorithm properly represent the vine travel patterns in searching K number of paths. A noticeable result is that the algorithm may be a promising alternative for ITS deployment by enabling to provide reasonable route information including perceived traveler costs.

• Journal of KIISE:Software and Applications
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• v.32 no.10
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• pp.949-955
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• 2005

In this Paper, we propose fast block matching algorithm by dividing complex areas based on complexity order of reference block and square sub-block to reduce an amount of computation of full starch(FS) algorithm for fast motion estimation, while keeping the same prediction quality compared with the full search algorithm. By using the fact that matching error is proportional to the gradient of reference block, we reduced unnecessary computations with square sub-block adaptive matching scan based image complexity instead of conventional sequential matching scan and row/column based matching scan. Our algorithm reduces about $30\%$ of computations for block matching error compared with the conventional partial distortion elimination(PDE) algorithm without any prediction quality, and our algorithm will be useful in real-time video coding applications using MPEG-4 AVC or MPEG-2.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.85-107
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• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.40-53
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• 2018

In smart card data, movement of railway passengers appears in order of smart card terminal ID. The initial terminal ID holds information on the entering station's tag-in railway line, the final terminal ID the exit station tag-out railway line, and the middle terminal ID the transfer station tag subway line. During the past, when the metropolitan city rail consisted of three public corporations (Seoul Metro, Incheon Transit Corporation, and Korail), OD data was expressed in two metrics of initial and final smart card terminal ID. Recently, with the entrance of private corporations like Shinbundang Railroad Corporation, and UI Corporation, inclusion of entering transfer line terminal ID and exiting transfer line terminal ID as part of Chain OD has become standard. Exact route construction using Chain OD has thus become integral as basic data for revenue allocation amongst metropolitan railway transport corporations. Accordingly, path detection in railway networks has evolved to an optimal path detection problem using Chain OD, hence calling for a renewed solution method. This research proposes an optimal path detection method between the initial terminal ID and final terminal ID of Chain OD terminal IDs within the railway network. Here, private line transfer TagIn/Out must be reflected in optimal path detection using Chain OD. To achieve this, three types of link-based optimum path detection methods are applied in order of 1. node-link, 2. link-link, 3. link-node. The method proposed based on additional path costs is shown to satisfy the optimal conditions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.4 no.2
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• pp.87-95
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• 2003

Dynamic Programming Matching (DPM) is a mathematical optimization technique for sequentially structured problems, which has, over the years, played a major role in providing primary algorithms in pattern recognition fields. Most practical applications of this method in signature verification have been based on the practical implementational version proposed by Sakoe and Chiba [9], and il usually applied as a case of slope constraint p = 0. We found, in this case, a modified version of DPM by applying a heuristic (forward seeking) implementation is more efficient, offering significantly reduced processing complexity as well as slightly improved verification performance.

• 한국경영정보학회:학술대회논문집
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• 2008.06a
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• pp.153-170
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• 2008

Customers' needs change every moment. Profitability of stores can't be increased anymore with an existing standardized chain store management. Accordingly, a personalized store management tool needs through prediction of customers' preference. In this study, we propose a recommending procedure using dynamic customers' preference by analyzing the transaction database. We utilize self-organizing map algorithm and association rule mining which are applied to cluster the chain stores and explore purchase sequence of customers. We demonstrate that the proposed methodology makes an effect on recommendation of products in the market which is characterized by a fast fashion and a short product life cycle.