• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.1
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• pp.54-68
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• 2001

This paper proposes a new algorithm that detects and separates the occluding and occluded objects in a 2D image. An input image is represented by the attributed graph where a node corresponds to a surface and an arc connecting two nodes describes the adjacency of the nodes in the image. Each end of arc is weighted by relation value which tells the number of edges connected to the surface represented by the node in the opposite side of the arc. In attributed graph, homogeneous nodes pertained to a same object always construct one of three special patterns which can be simply classified by comparison of relation values of the arcs. The experimental results have shown that the proposed algorithm efficiently separates the objects overlapped arbitrarily, and that this approach of separating objects before matching operation reduces the matching time significantly by simplifying the matching problem of overlapped objects as the one of individual single object.

• 한국태양에너지학회:학술대회논문집
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• 2012.03a
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• pp.419-424
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• 2012

This paper discusses the state-of-the-art techniques in real-time state estimation for the Smart Microgrids. The most popular method used in traditional power system state estimation is a Weighted Least Square(WLS) algorithm which is based on Maximum Likelihood(ML) estimation under the assumption of static system state being a set of deterministic variables. In this paper, we present a survey of dynamic state estimation techniques for Smart Microgrids based on Belief Propagation (BP) when the system state is a set of stochastic variables. The measurements are often too sparse to fulfill the system observability in the distribution network of microgrids. The BP algorithm calculates posterior distributions of the state variables for real-time sparse measurements. Smart Microgrids are modeled as a factor graph suitable for characterizing the linear correlations among the state variables. The state estimator performs the BP algorithm on the factor graph based the stochastic model. The factor graph model can integrate new models for solar and wind correlation. It provides the Smart Microgrids with a way of integrating the distributed renewable energy generation. Our study on Smart Microgrid state estimation can be extended to the estimation of unbalanced three phase distribution systems as well as the optimal placement of smart meters.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.1
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• pp.1-19
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• 2021

The graph-based tag recommendation algorithm FolkRank can effectively utilize the relationships between three entities, namely users, items and tags, and achieve better tag recommendation performance. However, FolkRank does not consider the internal relationships of user-user, item-item and tag-tag. This leads to the failure of FolkRank to effectively map the tagging behavior which contains user neighbors and item neighbors to a tripartite graph. For item-item relationships, we can dig out items that are very similar to the target item, even though the target item may not have a strong connection to these similar items in the user-item-tag graph of FolkRank. Hence this paper proposes an improved FolkRank algorithm named FolkRank++, which fully considers the user-user and item-item internal relationships in tag recommendation by adding the correlation information between users or items. Based on the traditional FolkRank algorithm, an initial weight is also given to target user and target item's neighbors to supply the user-user and item-item relationships. The above work is mainly completed from two aspects: (1) Finding items similar to target item according to the attribute information, and obtaining similar users of the target user according to the history behavior of the user tagging items. (2) Calculating the weighted degree of items and users to evaluate their importance, then assigning initial weights to similar items and users. Experimental results show that this method has better recommendation performance.

• Journal of KIISE:Software and Applications
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• v.35 no.11
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• pp.671-680
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• 2008

Motion capture devices have been utilized in producing several contents, such as movies and video games. However, since motion capture devices are expensive and inconvenient to use, motions segmented from captured data was recycled and synthesized to utilize it in another contents, but the motions were generally segmented by contents producers in manual. Therefore, automatic motion segmentation is recently getting a lot of attentions. Previous approaches are divided into on-line and off-line, where ow line approaches segment motions based on similarities between neighboring frames and off-line approaches segment motions by capturing the global characteristics in feature space. In this paper, we propose a graph-based high-level motion segmentation method. Since high-level motions consist of repeated frames within temporal distances, we consider similarities between neighboring frames as well as all similarities among all frames within the temporal distance. This is achieved by constructing a graph, where each vertex represents a frame and the edges between the frames are weighted by their similarity. Then, normalized cuts algorithm is used to partition the constructed graph into several sub-graphs by globally finding minimum cuts. In the experiments, the results using the proposed method showed better performance than PCA-based method in on-line and GMM-based method in off-line, as the proposed method globally segment motions from the graph constructed based similarities between neighboring frames as well as similarities among all frames within temporal distances.

• Journal of Korea Water Resources Association
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• v.51 no.11
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• pp.989-998
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• 2018

Connectivity between water source and demand node can be served as a critical system performance indicator of the degree of water distribution network (WDN)' failure severity under abnormal conditions. Graph theory-based approaches have been widely applied to quantify the connectivity due to WDN's graph-like topological feature. However, most previous studies used undirected-unweighted graph theory which is not proper to WDN. In this study, the directed-weighted graph theory was applied for WDN connectivity analyses. We also proposed novel connectivity indicators, Source-to-Node Shortest Pathway (SNSP) and SNSP-Degree (SNSP-D) which is an inverse of the SNSP value, that does not require complicate hydraulic simulation of a WDN of interest. The proposed SNSP-D index was demonstrated in total 42 networks in J City, South Korea in which Pearson Correlation Coefficient (PCC) between the proposed SNSP-D and four other system performance indicators was computed: three resilience indexes and an energy efficiency metric. It was confirmed that a system representative value of the SNSP-D has strong correlation with all resilience and energy efficiency indexes (PCC = 0.87 on average). Especially, PCC was higher than 0.93 with modified resilience index (MRI) and energy efficiency indicator. In addition, a multiple linear regression analysis was performed to identify the system hydraulic characteristic factors that affect the correlation between SNSP-D and other system performance indicators. The proposed SNSP is expected to be served as a useful surrogate measure of resilience and/or energy efficiency indexes in practice.

• Tribology and Lubricants
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• v.19 no.1
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• pp.15-20
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• 2003

The purpose of the paper is to investigate the effects of design parameters on the noise of a rotor- bearing system supported in oil lubricated journal bearings. Effects of radial clearance and width of the bearing, lubricant viscosity and mass eccentricity of the rotor are also examined. Numerical results of the parametric studies are summarized through graph for the A-weighted sound pressure level of the bearing with respect to the rotational speed of the rotor. Results show that the sound pressure level of the bearing is markedly influenced by the mass eccentricity of the rotor and the radial clearance and the width of the bearing. The high viscosity of the lubricant slightly decreases the noise of the bearing, but its effect is relatively very low at high speed. The results of the paper could be an aid in the low noise design of rotor-bearing system supported in oil lubricated journal bearings.

• The Journal of Information Systems
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• v.9 no.1
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• pp.27-44
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• 2000

The structure of neural networks is represented by a weighted directed graph with nodes representing units and links representing connections. Each link is assigned a numerical value representing the weight of the connection. In learning process, the values of weights are adjusted by errors. Following experiment results, the interval of adjusting weights, that is, epoch size influenced neural networks＇ performance. As epoch size is larger than a certain size, neural networks＇performance decreased drastically. And the number of hidden layer＇s node also influenced neural networks＇performance. The networks＇performance decreased as hidden layers have more nodes and then increased at some number of hidden layer＇s node. So, in implementing of neural networks the epoch size and the number of hidden layer＇s node should be decided by systematic methods, not empirical or heuristic methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.8
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• pp.1913-1925
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• 2012

In order to efficiently utilize limited radio resources, resource allocation schemes in OFDMA-based wireless networks have gained intensive attention recently. Instead of improving the throughput performance, the utility is adopted as the metric for resource allocation, which provides reasonable methods to build up the relationship between user experience and various quality-of-service (QoS) metrics. After formulating the optimization problem by using a weighted bipartite graph, a modified bipartite matching method is proposed to find a suboptimal solution for the resource allocation problem in OFDMA-based wireless systems with feasible computational complexity. Finally, simulation results are presented to validate the effectiveness of the proposed method.

• Journal of the Korea Computer Industry Society
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• v.5 no.9
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• pp.921-928
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• 2004

This paper proposed dynamic adaptive model based on discrete probability distribution function and user profile in web based HyperMedia educational systems. This modelsrepresents application domain to weighted direction graph of dynamic adaptive objects andmodeling user actions using dynamically approach method structured on discrete probability function. Proposed probabilitic analysis can use that presenting potential attribute to useractions that are tracing search actions of user in WebMedia structure. This approach methodscan allocate dynamically appropriate profiles to user.

• Genomics & Informatics
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• v.1 no.1
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• pp.20-24
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• 2003

The analysis of DNA microarry data is one of the most important things for functional genomics research. The matrix representation of microarray data and its successive 'optimal' incisional hyperplanes is a useful platform for developing optimization algorithms to determine the optimal partitioning of pairwise proximity matrix representing completely connected and weighted graph. We developed Deterministic Annealing (DA) approach to determine the successive optimal binary partitioning. DA algorithm demonstrated good performance with the ability to find the 'globally optimal' binary partitions. In addition, the objects that have not been clustered at small non­zero temperature, are considered to be very sensitive to even small randomness, and can be used to estimate the reliability of the clustering.