• Journal of Korea Society of Industrial Information Systems
• /
• v.28 no.6
• /
• pp.63-81
• /
• 2023

This study endeavors to classify and categorize similar policy programs through network clustering analysis, using textual information from data-related policy programs in Korea. To achieve this, descriptions of data-related budgetary programs in South Korea in 2022 were collected, and keywords from the program contents were extracted. Subsequently, the similarity between each program was derived using TF-IDF, and policy program network was constructed accordingly. Following this, the structural characteristics of the network were analyzed, and similar policy programs were clustered and categorized through network clustering. Upon analyzing a total of 97 programs, 7 major clusters were identified, signifying that programs with analogous themes or objectives were categorized based on application area or services utilizing data. The findings of this research illuminate the current status of data-related policy programs in Korea, providing policy implications for a strategic approach to planning future national data strategies and programs, and contributing to the establishment of evidence-based policies.

• Journal of Korea Multimedia Society
• /
• v.13 no.6
• /
• pp.911-923
• /
• 2010

In wireless sensor networks (WSNs), cluster based data routing protocols have the advantages of reducing energy consumption and link maintenance cost. Unfortunately, most of clustering protocols have been designed for uniformly distributed sensor networks. However, some urgent situations do not allow thousands of sensor nodes being deployed uniformly. For example, air vehicles or balloons may take the responsibility for deploying sensor nodes hence leading a normally distributed topology. In order to improve energy efficiency in such sensor networks, in this paper, we propose a new cluster formation algorithm named DAEEC (Density Aware Energy-Efficient Clustering). In this algorithm, we define two kinds of clusters: Low Density (LD) clusters and High Density (HD) clusters. They are determined by the number of nodes participated in one cluster. During the data routing period, the HD clusters help the neighbor LD clusters to forward the sensed data to the central base station. Thus, DAEEC can distribute the energy dissipation evenly among all sensor nodes by considering the deployment density to improve network lifetime and average energy savings. Moreover, because the HD clusters are densely deployed they can work in a manner of our former algorithm EEVAR (Energy Efficient Variable Area Routing Protocol) to save energy. According to the performance analysis result, DAEEC outperforms the conventional data routing schemes in terms of energy consumption and network lifetime.

• International journal of advanced smart convergence
• /
• v.9 no.2
• /
• pp.185-194
• /
• 2020

Designing of a hierarchical clustering algorithm is one of the numerous approaches to minimize the energy consumption of the Wireless Sensor Networks (WSNs). In this paper, a homogeneous and randomly deployed sensor nodes is considered. These sensors are energy constrained elements. The nominal selection of the Cluster Head (CH) which falls under the clustering part of the network protocol is studied and compared to Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. CHs in this proposed process is the function of total remaining energy of each node as well as total average energy of the whole arrangement. The algorithm considers initial energy, optimum value of cluster heads to elect the next group of cluster heads for the network as well as residual energy. Total remaining energy of each node is compared to total average energy of the system and if the result is positive, these nodes are eligible to become CH in the very next round. Analysis and numerical simulations quantify the efficiency and Average Energy Ratio (AER) of the proposed system.

• International Journal of Computer Science & Network Security
• /
• v.22 no.7
• /
• pp.75-84
• /
• 2022

The efficiency of the regional fiscal policy implementation is based on the achievement of target criteria in the formation and distribution of own financial resources of local budgets, reducing their deficit and reducing dependence on transfers. It is also relevant to compare the development of financial autonomy of regions in the course of decentralisation of fiscal relations. The study consists in the cluster analysis of the effectiveness of fiscal policy implementation in the context of 24 regions and the capital city of Kyiv (except for temporarily occupied territories) under conditions of fiscal decentralisation. Clustering of the regions of Ukraine by 18 indicators of fiscal policy implementation efficiency was carried out using Ward's minimum variance method and k-means clustering algorithm. As a result, the regions of Ukraine are grouped into 5 homogeneous clusters. For each cluster measures were developed to increase own revenues and minimize dependence on official transfers to increase the level of financial autonomy of the regions. It has been proved that clustering algorithms are an effective tool in assessing the effectiveness of fiscal policy implementation at the regional level and stimulating further expansion of financial decentralisation of regions.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.13 no.6
• /
• pp.1222-1227
• /
• 2009

In this paper, we proposed a new method, the CACH(Context-aware Clustering Hierarchy) algorithm in Mobile Ad-hoc Network(MANET) systems. The proposed CACH algorithm based on hybrid and clustering protocol that provide the reliable monitoring and control of a variety of environments for remote place. To improve the routing protocol in MANET, energy efficient routing protocol would be required as well as considering the mobility would be needed. The proposed analysis could help in defining the optimum depth of hierarchy architecture CACH utilize. Also, the proposed CACH could be used localized condition to enable adaptation and robustness for dynamic network topology protocol and this provide that our hierarchy to be resilient. As a result, our simulation results would show that a new method for CACH could find energy efficient depth of hierarchy of a cluster.

• Communications for Statistical Applications and Methods
• /
• v.21 no.3
• /
• pp.225-234
• /
• 2014

We propose a novel hierarchical clustering for distribution valued dissimilarities. Analysis of large and complex data has attracted significant interest. Symbolic Data Analysis (SDA) was proposed by Diday in 1980's, which provides a new framework for statistical analysis. In SDA, we analyze an object with internal variation, including an interval, a histogram and a distribution, called a symbolic object. In the study, we focus on a cluster analysis for distribution valued dissimilarities, one of the symbolic objects. A hierarchical clustering has two steps in general: find out step and update step. In the find out step, we find the nearest pair of clusters. We extend it for distribution valued dissimilarities, introducing a measure on their order relations. In the update step, dissimilarities between clusters are redefined by mixture of distributions with a mixing ratio. We show an actual example of the proposed method and a simulation study.

• Atmosphere
• /
• v.16 no.4
• /
• pp.259-267
• /
• 2006

In the present study, the neural network (NN) model with cluster analysis method was developed to predict storm surge in the whole Korean coastal regions with special focuses on the regional extension. The model used in this study is NN model for each cluster (CL-NN) with the cluster analysis. In order to find the optimal clustering of the stations, agglomerative method among hierarchical clustering methods was used. Various stations were clustered each other according to the centroid-linkage criterion and the cluster analysis should stop when the distances between merged groups exceed any criterion. Finally the CL-NN can be constructed for predicting storm surge in the cluster regions. To validate model results, predicted sea level value from CL-NN model was compared with that of conventional harmonic analysis (HA) and of the NN model in each region. The forecast values from NN and CL-NN models show more accuracy with observed data than that of HA. Especially the statistics analysis such as RMSE and correlation coefficient shows little differences between CL-NN and NN model results. These results show that cluster analysis and CL-NN model can be applied in the regional storm surge prediction and developed forecast system.

• Journal of Korea Multimedia Society
• /
• v.14 no.10
• /
• pp.1287-1302
• /
• 2011

Clustering protocol that is used in wireless sensor network is an efficient method that extends the lifetime of the network. However, when this method is applied to an environment in which collected data of the sensor node easily overlap, sensor nodes unnecessarily consumes energy. In the case of clustering technique that uses a threshold, the lifetime of the network is extended but the degree of accuracy of collected data is low. Therefore it is hard to trust the data and improvement is needed. In addition, it is hard for the clustering protocol that uses multi-hop transmission to normally collect data because the selection of a cluster head node occurs at random and therefore the link of nodes is often disconnected. Accordingly this paper suggested a cluster-formation algorithm that reduces unnecessary energy consumption and that works with an alleviated link disconnection. According to the result of performance analysis, the suggested method lets the nodes consume less energy than the existing clustering method and the transmission efficiency is increased and the entire lifetime is prolonged by about 30%.

• Journal of Korea Multimedia Society
• /
• v.12 no.11
• /
• pp.1609-1622
• /
• 2009

In wireless sensor networks, a clustering protocol is an efficient method to prolong network lifetime. In general, it results in more energy consumption at the cluster-head node. Hence, such a protocol must changes the cluster formation and cluster-head node in each round to prolong the network lifetime. But, this method also causes large amount of energy consumption during the set-up process of cluster formation. In order to improve energy efficiency, in this paper, we propose a new clustering algorithm. In this algorithm, we exclude duplicated data of adjacent nodes and transmits the threshold value. We define a group as the sensor nodes within close proximity of each other. In a group, a node senses and transmits data at a time on the round-robin basis. In a view of whole network, group is treated as one node. During the setup phase of a round, intra clusters are formed first and then they are re-clustered(network cluster) by choosing cluster-heads(group). In the group with a cluster-head, every member node plays the role of cluster-head on the round-robin basis. Hence, we can lengthen periodic round by a factor of group size. As a result of analysis and comparison, our scheme reduces energy consumption of nodes, and improve the efficiency of communications in sensor networks compared with current clustering methods.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.12 no.6
• /
• pp.577-582
• /
• 2002

Neural network can be trained to approximate an arbitrary nonlinear function of multivariate data like the mini-mill crown values in Automatic Shape Control. The trained weights of neural network can evaluate or generalize the process data outside the training vectors. Sometimes, the blind modeling of the process data is necessary to compare with the scattered analytical model of mini-mill process in isolated electro-mechanical forms. To come up with a viable model, we propose the blind neural-based range-division domain-clustering piecewise-linear modeling scheme. The basic ideas are: 1) dividing the range of target data, 2) clustering the corresponding input space vectors, 3)training the neural network with clustered prototypes to smooth out the convergence and 4) solving the resulting matrix equations with a pseudo-inverse to alleviate the ill-conditioning problem. The simulation results support the effectiveness of the proposed scheme and it opens a new way to the data analysis technique. By the comparison with the statistical regression, it is evident that the proposed scheme obtains better modeling error uniformity and reduces the magnitudes of errors considerably. Approximatly 10-fold better performance results.