• The KIPS Transactions:PartC
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• v.11C no.5
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• pp.645-652
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• 2004

In this paper, we aggregate multi-links information between boundary nodes using the line segment scheme that aggregates topology in-formation within PG referring bandwidth and delay parameter. The proposed scheme can search multi-links efficiently using the depth priority method based on hop count instead of searching all links. To do this, we propose a modified line segment algorithm using two line segment method that represents two points which consist of delay-bandwidth pair to reduce topology information and provide a flexibility to the multi pie-links aggregation. And we apply it to current star topology aggregation. To evaluate performance of the proposed scheme, we compare/analyze the current method with the proposed scheme with respect to call success rate, access time and crankback rate. Through the simulation result analysis, the proposed star topology aggregation scheme presents the better performance than existing scheme.

• Journal of The Korean Astronomical Society
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• v.37 no.2
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• pp.79-85
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• 2004

We developed an algorithm to identify and determine periods of variable sources. With its robustness and high speed, it is expected to become an useful tool for surveys with large volume of data. This new scheme consists of an initial coarse. process of finding several candidate periods followed by a secondary process of much finer period search. With this multi-step approach, best candidates among statistically possible periods are produced without human supervision and also without any prior assumption on the nature of the variable star in question. We tested our algorithm with 381 stars taken from the ASAS survey and the result is encouraging. In about $76\%$ cases, our results are nearly identical as their published periods. Our algorithm failed to provide convincing periods for only about $10\%$ cases. For the remaining $14\%$, our results significantly differ from their periods. We show that, in many of these cases, our periods are superior and much closer to the true periods. However, the existence of failures, and also periods sometimes worse than manually controlled results, indicates that this algorithm needs further improvement. Nevertheless, the present experiment shows that this is a positive step toward a fully automated period analysis for future variability surveys.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.5
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• pp.425-433
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• 2012

Dijkstra's algorithm is one of well-known methods to find shortest paths over a network. However, more research on $A^*$ algorithm is necessary to discover the shortest route to a goal point with the heuristic information rather than Dijkstra's algorithm which aims to find a path considering only the shortest distance to any point for an optimal path search. Therefore, in this paper, we compared Dijkstra's algorithm and $A^*$ algorithm for bicycle route selection. For this purpose, the horizontal distance according to slope angle and average speed were calculated based on factors which influence bicycle route selection. And bicycle routes were selected considering the shortest distance or time-dependent shortest path using Dijkstra's or $A^*$ algorithm. The result indicated that the $A^*$ algorithm performs faster than Dijkstra's algorithm on processing time in large study areas. For the future, optimal path selection algorithm can be used for bicycle route plan or a real-time mobile services.

• Journal of Computing Science and Engineering
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• v.1 no.2
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• pp.177-194
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• 2007

Index selection is one of the most important decisions to take in the physical design of relational data warehouses. Indices reduce significantly the cost of processing complex OLAP queries, but require storage cost and induce maintenance overhead. Two main types of indices are available: mono-attribute indices (e.g., B-tree, bitmap, hash, etc.) and multi-attribute indices (join indices, bitmap join indices). To optimize star join queries characterized by joins between a large fact table and multiple dimension tables and selections on dimension tables, bitmap join indices are well adapted. They require less storage cost due to their binary representation. However, selecting these indices is a difficult task due to the exponential number of candidate attributes to be indexed. Most of approaches for index selection follow two main steps: (1) pruning the search space (i.e., reducing the number of candidate attributes) and (2) selecting indices using the pruned search space. In this paper, we first propose a data mining driven approach to prune the search space of bitmap join index selection problem. As opposed to an existing our technique that only uses frequency of attributes in queries as a pruning metric, our technique uses not only frequencies, but also other parameters such as the size of dimension tables involved in the indexing process, size of each dimension tuple, and page size on disk. We then define a greedy algorithm to select bitmap join indices that minimize processing cost and verify storage constraint. Finally, in order to evaluate the efficiency of our approach, we compare it with some existing techniques.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.4
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• pp.1693-1713
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• 2018

An important technique used by database administrators (DBAs) is to improve performance in decision-support workloads associated with a Star schema is multi-level partitioning. Queries will then benefit from performance improvements via partition elimination, due to constraints on queries expressed on the dimension tables. As the task of multi-level partitioning can be overwhelming for a DBA we are proposing a wizard that facilitates the task by calculating a partitioning scheme for a particular workload. The system resides completely on a client and interacts with the costing estimation subsystem of the query optimizer via an API over the network, thereby eliminating any need to make changes to the optimizer. In addition, since only cost estimates are needed the wizard overhead is very low. By using a greedy algorithm for search space enumeration over the query predicates in the workload the wizard is efficient with worst-case polynomial complexity. The technology proposed can be applied to any clustering or partitioning scheme in any database management system that provides an interface to the query optimizer. Applied to the Teradata database the technology provides recommendations that outperform a human expert's solution as measured by the total execution time of the workload. We also demonstrate the scalability of our approach when the fact table (and workload) size increases.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.7
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• pp.1231-1237
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• 2022

In the present study, the optimization of the main particulars of a ship using AI-based design search techniques was investigated. For the design search techniques, the SHERPA algorithm by HEEDS was applied, and CFD analysis using STAR-CCM+ was applied for the calculation of resistance performance. Main particulars were automatically transformed by modifying the main particulars of the ship at the stage of preprocessing using JAVA script and Python. Small catamaran was chosen for the present study, and the main dimensions of the length, breadth, draft of demi-hull, and distance between demi-hulls were considered as design variables. Total resistance was considered as an objective function, and the range of displaced volume considering the arrangement of the outfitting system was chosen as the constraint. As a result, the changes in the individual design variables were within ±5%, and the total resistance of the optimized hull form was decreased by 11% compared with that of the existing hull form. Throughout the present study, the resistance performance of small catamaran could be improved by the optimization of the main dimensions without direct modification of the hull shape. In addition, the application of optimization using design search techniques is expected for the improvement in the resistance performance of a ship.

• The Bulletin of The Korean Astronomical Society
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• v.40 no.2
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• pp.48.2-48.2
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• 2015

Galaxy Clusters with complex inner structures are excellent laboratories with which to study the properties of galaxies and the groups of galaxies in them. To execute a systematic search for flux-limited galaxy groups and clusters based on the spectroscopic galaxies with r < 17.77 of SDSS data release 12, we adopt a modified version of the friends-of-friends algorithm, whereupon a total of 3272 galaxy groups and clusters with at least 10 members are found. In this study, we aim to assign galaxy subgroups within groups and clusters that enable us to investigate the detained star-formation history of galaxies by applying a modified hierarchical grouping method to our galaxy group and cluster catalog. We note that roughly 70% of our galaxy groups and clusters have subgroups. The most remarkable additional results are as follows. The brightest cluster galaxies (BCGs) have brighter luminosities with larger velocity dispersions of groups and clusters. The BCGs are concentrated toward the most massive subgroups than the second and third one. This result implies that the galaxy properties can be affected by different merger and star-formation histories for differing environments.

• Bulletin of the Korean Space Science Society
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• 2009.10a
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• pp.29.4-30
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• 2009

Extra-tidal feature of the globular clusters such as tidal tails and halos can be a crucial evidence of the merging scenario of the Galaxy formation in the dynamical point of view. To search for such an extra-tidal feature of globular clusters located in the Galactic bulge(RGC<3kpc), we obtained wide-field near-infrared JHKs images of 6 metal-poor ([Fe/H]<-1.0) clusters and 3 metal-rich ([Fe/H]>-1.0) clusters. Observations were carried out using IRSF 1.4m telescope and SIRIUS near-infrared camera, during 2006~2007. The obtained images have a total maximum field-of-view of ~ $21'\times 21'$. To select clusters' member stars and minimize the field star contaminations, we applied CMD masking algorithm. Smoothed surface density contour maps with selected stars for each cluster show overdensity features around the tidal radius and beyond. Also, radial surface density profiles within the tidal radius of the clusters show an overdensity feature as a change of slope of the radial profile. The results add further observational constraints of the formation of the Galactic bulge.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.1C
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• pp.89-96
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• 2012

Since tactical robots are going to be grown and tactical data communications will be more network-centric, the reliability of wireless tactical data networks is going to be very important in the future. However, the connectivity of such wireless tactical data networks can be extremely uncertain in practical circumstances. In this paper, we propose a searching technique to find out the weak boundary area of the network connectivity using a small UAV(unmanned aerial vehicle) which has a simple polling access function to wireless nodes on the ground in wireless tactical data networks. The UA V calculates the network topology of the wireless tactical data networks and coverts it to the Lapalcian matrix. In the proposed algorithm, we iteratively search the eigenvalues and find a minimum cut in the network resulting in finding the weak boundary of the connectivity for the wireless tactical data networks. If a UAV works as a relay nodes for the weak area, we evaluate that the throughput performance of the proposed algorithm outperforms star connection method and MST(minimum Spanning Tree) connection method. The proposed algorithm can be applied for recovering the connectivity of wireless tactical data networks.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.137-148
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• 2014

Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used