• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.89-98
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• 2024

In this paper, we propose a system for recommending lecture videos at the chapter level, addressing the balance between accuracy and processing speed in chapter-level video recommendations. Specifically, it has been observed that enhancing recommendation accuracy reduces processing speed, while increasing processing speed decreases accuracy. To mitigate this trade-off, a hybrid approach is proposed, utilizing techniques such as TF-IDF, k-means++ clustering, and Graph Neural Networks (GNN). The approach involves pre-constructing clusters based on chapter similarity to reduce computational load during recommendations, thereby improving processing speed, and applying GNN to the graph of clusters as nodes to enhance recommendation accuracy. Experimental results indicate that the use of GNN resulted in an approximate 19.7% increase in recommendation accuracy, as measured by the Mean Reciprocal Rank (MRR) metric, and an approximate 27.7% increase in precision defined by similarities. These findings are expected to contribute to the development of a learning system that recommends more suitable video chapters in response to learners' queries.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.628-634
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• 1995

In the area of assembly process of micro-chips and electronic parts on the printed circuit board, surface mounting device(SMD) is used as a fundamental tool. Generally speaking, the motion of the SMD is based on the ball screw system operated by any type of actuators. The ball screw system is a mechanical transformer which converts the mechanical rotational motion to the translational one. Also, this system could be considered as an efficient motion device against mechanical backash and friction. Therefore a dynamic modeling and stste sensitivity analysis of the ball screw system in SMD have to be done in the initial design stage. In this paper, a simple mathematical dynamic model for this system and the sensitivity snalysis are mentioned. Especially, the bond graph approach is used for graphical modeling of the dynamic system before analysis stage. And the direct differentiation method is used for the state sensitivity analysis of the system. Finally, some trends for the state variables with respect to the design variables could be suggested for the better design based on the results on the results of dynamic and state sensitivity.

• Journal of Internet Computing and Services
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• v.3 no.6
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• pp.79-89
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• 2002

The performance of OCC is very sensitive to the transaction abort rate, Even if the abort probability can be reduced by re-ordering, most of re-ordering schemes have space overhead in maintaining a graph or histories, In this paper, we proposed an efficient re-ordering scheme based on a broadcast invalidation mechanism. Our scheme, called CBI, can reduce the abort probability without space overhead in maintaining a graph or histories, By simulation studies, we showed that CBI reduced the abort rate and unnecessary operations, Additionally, we showed that CBI outperforms not only BTS but also TSH with low space overhead.

• Genomics & Informatics
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• v.17 no.1
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• pp.4.1-4.7
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• 2019

In this paper, we propose a window-based mechanism visualization approach as an alternative way to measure the seriousness of the difference among data-insights extracted from a composite biodata point. The approach is based on two components: undirected graph and Mosaab-metric space. The significant application of this approach is to visualize the segmented genome of a virus. We use Influenza and Ebola viruses as examples to demonstrate the robustness of this approach and to conduct comparisons. This approach can provide researchers with deep insights about information structures extracted from a segmented genome as a composite biodata point, and consequently, to capture the segmented genetic variations and diversity (variants) in composite data points.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.120.3-120
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• 2001

We propose a new method of path planning for cleaning robots. Path planning problem for cleaning robots is different from conventional path planning problems in which finding a collision-free trajectory from a start point to a goal point is focused. In the case of cleaning robots, however, a planned path should cover all area to be cleaned. To resolve this problem in a systematic way, we propose a method based on a graph model as follows: at first, partition a given map into proper regions, then transform a divided region to a vertex and a connectivity between regions to an edge of a graph. Finally, a region is divided into sub-regions so that the graph has a unary tree which is the simplest Hamilton path. The effectiveness of the proposed method is shown by computer simulation results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.10
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• pp.2650-2662
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• 2012

Due to the semantic gap issue, the performance of automatic image annotation is still far from satisfactory. Active learning approaches provide a possible solution to cope with this problem by selecting most effective samples to ask users to label for training. One of the key research points in active learning is how to select the most effective samples. In this paper, we propose a novel active learning approach based on sparse graph. Comparing with the existing active learning approaches, the proposed method selects the samples based on two criteria: uncertainty and representativeness. The representativeness indicates the contribution of a sample's label propagating to the other samples, while the existing approaches did not take the representativeness into consideration. Extensive experiments show that bringing the representativeness criterion into the sample selection process can significantly improve the active learning effectiveness.

• Industrial Engineering and Management Systems
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• v.10 no.4
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• pp.238-246
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• 2011

Given a directed graph, we can determine how the user's preference moves from one product item to another. In this graph called "preference transition network", each node represents the product item while its edge pointing to the other nodes represents the transition of user's preference. However, with the large number of items make the network become more complex, unclear and difficult to be interpreted. In order to address this problem, this paper proposes a visualization technique in preference transition analysis based on recency and frequency. By adapting these two elements, the semantic meaning of each item and its transition can be clearly identified by its different types of node size, color and edge style. The experiment in a sales data has shown the results of the proposed approach.

• Journal of Korean Port Research
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• v.12 no.1
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• pp.55-64
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• 1998

Directed graphic algorithm was applied to an empirical analysis of traffic occupant fatalities based on a model by Crandall. In this paper, Crandall's data on U.S. traffic fatalities for the period 1947-1981 are focused and extended to include 1982-1993. Based on the 1947-1981 annual data, the directed graph algorithms reveal that occupant traffic deaths are directly caused by income, vehicle miles, and safety devices. Vehicle mileage is caused by income and rural driving. The estimation is conducted using three stage least squares regression. Those results show a difference between the traditional regression methodology and causal graphical analysis. It is also found that forecasts from the directed graph based model outperform forecasts from the regression-based models, in terms of mean squared forecasts error. Furthermore, it is demonstrates that there exists some latent variables between all explanatory variables and occupant deaths.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.04a
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• pp.185-188
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• 2005

This paper suggests a control method for efficient topology/parameter evolution in a bond-graph-based GP design framework that automatically synthesizes designs for multi-domain, lumped parameter dynamic systems, We adopt a hierarchical breeding control mechanism with fitness-level-dependent differences to obtain better balancing of topology/parameter search - biased toward topological changes at low fitness levels, and toward parameter changes at high fitness levels. As a testbed for this approach, an eigenvalue assignment problem, which is to find bond graph models exhibiting minimal distance errors from target sets of eigenvalues, was tested and showed improved performance for various sets of eigenvalues.

• Journal of KIISE:Information Networking
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• v.31 no.2
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• pp.123-130
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• 2004

Routing and wavelength assignment(RWA) problem is an important issue in optical transport networks based on wavelength division multiplexing(WDM) technique. It is typically solved using a combination of linear programming and graph coloring, or path selection based graph algorithms. Such methods are either complex or make extensive use of heuristics. In this paper we propose a novel and efficient approach which basically obtains the maximum edge disjoint paths (EDPs) for each source-destination demand pair. And those EDPs obtained are stored in Lookup Table and used for the update of weight matrix. Routes are determined in order by the weight matrix for the demand set. The comprehensive computer simulation shows that the Proposed algorithm uses similar or fewer wavelengths with significantly less execution time than bounded greedy approach (BGA) for EDP which is currently known to be effective in practice.