• Journal of the Korea Society of Computer and Information
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• v.13 no.4
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• pp.105-113
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• 2008

While the wireless sensor network has a strong point which does not have effect on whole activities of network even though neighboring sensor nods fail activities of some sensor nod or make some functions disappear by the characteristic of similar information detection, it has problems which is slowing down of wireless medium, transfer character with severe error, limited power supply, the impossibility of change by optional arrangement of sensor nods etc. This paper proposes PRML techniques which performs the fittest course searching process to reduce power consumption of entire nods while guarantees the scalability of network organizing sensor nods hierarchically. The proposed technique can scatter the load of cluster head by considering the connectivity with surplus energy of nod and reduce the frequency of communication among the nods. As a result of the analysis in comparison with LEACH-C and HEED technique, PRML technique get efficiency of average 6.4% in energy consuming respect of cluster head, efficiency of average 8% in entire energy consuming respect, and more efficiency of average 7.5% in other energy consuming distribution of network scalability than LEACH-C and HEED technique.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.11
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• pp.1477-1483
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• 2020

The stability guarantee of wireless network based control systems is still challenging due to the lossy links and node failures. This paper proposes a hierarchical cluster-based network protocol called robust wireless sensor and actuator network (R-WSAN) by combining time, channel, and space resource diversity. R-WSAN includes a scheduling algorithm to support the network resource allocation and a control task sharing scheme to maintain the control stability of multiple plants. R-WSAN was implemented on a real test-bed using Zolertia RE-Mote embedded hardware platform running the Contiki-NG operating system. Our experimental results demonstrate that R-WSAN provides highly reliable and robust performance against lossy links and node failures. Furthermore, the proposed scheduling algorithm and the task sharing scheme meet the stability requirement of control systems, even if the controller fails to support the control task.

• Journal of Korea Port Economic Association
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• v.34 no.4
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• pp.39-52
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• 2018

The Seosan-Daesan Port is a representative trade port in Chungnam, and has the sixth largest total cargo throughput and the third largest oil cargo throughput in Korea. However, research on this port's development is lacking relative to that for Busan Port, Incheon Port, and Gwangyang Port, and no study exists that suggests the direction of the development strategy for Seosan-Daesan Port. This study discusses the future role of Seosan-Daesan Port in preparation for a rapidly changing future and the development strategy that should be established. Using the AHP, a development strategy is provided for Seosan-Daesan Port from short/mid-term and long-term viewpoints for three aspects: operation activation, infrastructure construction, and policy support. Operation activation is chosen as the most significant factor from a short/mid-term viewpoint, whereas infrastructure construction is recognized as important from a long-term viewpoint. Specifically, from a short/mid-term viewpoint, sustainable container cargo attraction, multipurpose dock construction, management pier construction, and opening of international passenger ferry lines are important factors while from the long-term viewpoint, hinterland construction, petrochemical industry cluster construction, automobile industry cluster construction, and management improvement system are important. Establishing action plans for each strategy and a cooperative network for sharing goals and strengthening cooperation is necessary.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.6
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• pp.1214-1221
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• 2007

Ad-hoc network is soft wireless communication network that is consisted of mobile node and clusters without helping of infrastructure. We propose a new ad hoc multicast routing protocol for based on the ontology scheme called inference network. Ontology knowledge-based is one of the structure of context-aware. Proposed structure is consisted of context awareness parameters as like distance between each nodes. The proposed architecture performs two types of routing discovery. One is Flooding Discovery Routing(FDR) for comparing analysis step and Local Discovery Routing(LDR) to compose path of node forecast(preservation) step from node's state value. The inference network structure of proposed RODMRP(Resilient Ontology-based Dynamic Multicast Routing Protocol) adopts a tree structure to enhance an efficient packet in various environment between mobile node. We will have developed an algorithm that will desist multi-hierarchy Layered networks to simulate a desired system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.12
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• pp.9-16
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• 2016

Wireless sensor networks (WSN) are composed of sensor nodes and a base station. The sensor nodes deploy a non-accessible area, receive critical information, and transmit it to the base station. The information received is applied to real-time monitoring, distribution, medical service, etc.. Recently, the WSN was extended to mobile wireless sensor networks (MWSN). The MWSN has been applied to wild animal tracking, marine ecology, etc.. The important issues are mobility and energy consumption in MWSN. Because of the limited energy of the sensor nodes, the energy consumption for data transmission affects the lifetime of the network. Therefore, efficient data transmission from the sensor nodes to the base station is necessary for sensing data. This paper, proposes an energy consumption model using two-tier clustering in mobile sensor networks (TTCM). This method divides the entire network into two layers. The mobility problem was considered, whole energy consumption was decreased and clustering methods of recent researches were analyzed for the proposed energy consumption model. Through analysis and simulation, the proposed TTCM was found to be better than the previous clustering method in mobile sensor networks at point of the network energy efficiency.

• KIISE Transactions on Computing Practices
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• v.21 no.7
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• pp.494-499
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• 2015

As the size of data increases, it becomes important to identify properties by analyzing big data. In this paper, we propose a k-Means based efficient clustering technique, called MCSKMeans (Multi centroid set k-Means), using distributed parallel processing framework MapReduce. A problem with the k-Means algorithm is that the accuracy of clustering depends on initial centroids created randomly. To alleviate this problem, the MCSK-Means algorithm reduces the dependency of initial centroids using sets consisting of k centroids. In addition, we apply the agglomerative hierarchical clustering technique for creating k centroids from centroids in m centroid sets which are the results of the clustering phase. In this paper, we implemented our MCSK-Means based on the MapReduce framework for processing big data efficiently.

• Journal of Practical Agriculture & Fisheries Research
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• v.23 no.1
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• pp.89-104
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• 2021

Based on the TF-IDF weighted value that evaluates the importance of words that play a key role, the semantic network analysis(SNA) was conducted on the self-introduction letter of freshman at Korea National College of Agriculture and Fisheries(KNCAF) in 2020. The top three words calculated by TF-IDF weights were agriculture, mathematics, study (Q. 1), clubs, plants, friends (Q. 2), friends, clubs, opinions, (Q. 3), mushrooms, insects, and fathers (Q. 4). In the relationship between words, the words with high betweenness centrality are reason, high school, attending (Q. 1), garbage, high school, school (Q. 2), importance, misunderstanding, completion (Q.3), processing, feed, and farmhouse (Q. 4). The words with high degree centrality are high school, inquiry, grades (Q. 1), garbage, cleanup, class time (Q. 2), opinion, meetings, volunteer activities (Q.3), processing, space, and practice (Q. 4). The combination of words with high frequency of simultaneous appearances, that is, high correlation, appeared as 'certification - acquisition', 'problem - solution', 'science - life', and 'misunderstanding - concession'. In cluster analysis, the number of clusters obtained by the height of cluster dendrogram was 2(Q.1), 4(Q.2, 4) and 5(Q. 3). At this time, the cohesion in Cluster was high and the heterogeneity between Clusters was clearly shown.

• The Journal of the Korea Contents Association
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• v.14 no.11
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• pp.467-475
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• 2014

With the development of genomics, wearable device and IT/NT, a vast amount of bio-medical data are generated recently. Also, healthcare industries based on big-data are booming and big-data technology based on bio-medical data is rising rapidly as a core technology for improving the national health and aged society. A pathway is the biological deep knowledge that represents the relations of dynamics and interaction among proteins, genes and cells by a network. A pathway is wildly being used as an important part of a bio-medical big-data analysis. However, a pathway analysis requires a lot of time and effort because a pathway is very diverse and high volume. Also, multidimensional analysis systems for various pathways are nonexistent even now. In this paper, we proposed a pathway analysis system that collects user interest pathways from KEGG pathway database that supports the most widely used pathways, constructs a network based on a hierarchy structure of pathways and analyzes the relations of dynamics and interaction among pathways by clustering and selecting core pathways from the network. Finally, to verify the superiority of our pathway analysis system, we evaluate the performance of our system in various experiments.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.3
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• pp.127-143
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• 2022

Recently, many attempts to run numerical ocean models in cloud computing environments have been tried actively. A cloud computing environment can be an effective means to implement numerical ocean models requiring a large-scale resource or quickly preparing modeling environment for global or large-scale grids. Many commercial and private cloud computing systems provide technologies such as virtualization, high-performance CPUs and instances, ether-net based high-performance-networking, and remote direct memory access for High Performance Computing (HPC). These new features facilitate ocean modeling experimentation on commercial cloud computing systems. Many scientists and engineers expect cloud computing to become mainstream in the near future. Analysis of the performance and features of commercial cloud services for numerical modeling is essential in order to select appropriate systems as this can help to minimize execution time and the amount of resources utilized. The effect of cache memory is large in the processing structure of the ocean numerical model, which processes input/output of data in a multidimensional array structure, and the speed of the network is important due to the communication characteristics through which a large amount of data moves. In this study, the performance of the Regional Ocean Modeling System (ROMS), the High Performance Linpack (HPL) benchmarking software package, and STREAM, the memory benchmark were evaluated and compared on commercial cloud systems to provide information for the transition of other ocean models into cloud computing. Through analysis of actual performance data and configuration settings obtained from virtualization-based commercial clouds, we evaluated the efficiency of the computer resources for the various model grid sizes in the virtualization-based cloud systems. We found that cache hierarchy and capacity are crucial in the performance of ROMS using huge memory. The memory latency time is also important in the performance. Increasing the number of cores to reduce the running time for numerical modeling is more effective with large grid sizes than with small grid sizes. Our analysis results will be helpful as a reference for constructing the best computing system in the cloud to minimize time and cost for numerical ocean modeling.