• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.12
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• pp.6081-6088
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• 2012

An unequal clustering method in wireless sensor networks is the technique that forms the cluster of different size. This method decreases whole energy consumption by solving the hot spot problem. In this paper, I propose a layer-based dynamic unequal clustering using the unequal clustering model. This method decreases whole energy consumption and maintain that equally using optimal cluster's number and cluster head position. I also show that proposed method is better than previous clustering method at the point of network lifetime.

• Journal of Preventive Medicine and Public Health
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• v.33 no.1
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• pp.51-55
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• 2000

Objectives . In large-scale field trials, randomization by cluster is frequently used because of the administrative convenience, a desire to reduce the effect of treatment contamination, and the need to avoid ethical issues that might of otherwise arise. Cluster randomization trials are experiments in which intact social unit, e.g., families, schools, cities, rather than independent individuals are randomly allocated to intervention groups. The positive correlation among responses of subjects from the same cluster is in matter in cluster randomization. This thesis is to compare the results of three randomization methods by standard error of estimator of treatment effect. Methods : We simulated cholesterol data varing the size of the cluster and the level of the correlation in clusters and analyzed the effect of cholesterol-lowering agent. Results : In intra-cluster randomization the standard error of the estimator of treatment effect is smallest relative to that in inter-cluster randomization and that in individual randomization. Conclusions : Infra-cluster randomization is the most efficient in its standard error of estimator of treatment effect but other factor should be considered when selecting a specific randomization method.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.4
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• pp.306-311
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• 2020

This paper deals with how to improve the performance of Python language with various libraries when developing a model using big data. The Python language uses the Pandas library for processing spreadsheet-format data such as Excel. In processing data, Python operates on an in-memory basis. There is no performance issue when processing small scale of data. However, performance issues occur when processing large scale of data. Therefore, this paper introduces a method for distributed processing of execution tasks in a single cluster and multiple clusters by using a Dask library that can be used with Pandas when processing data. The experiment compares the speed of processing a simple exponential model using only Pandas on the same specification hardware and the speed of processing using a dask together. This paper presents a method to develop a model by distributing a large scale of data by CPU cores in terms of performance while maintaining that python's advantage of using various libraries is easy.

• The Journal of Internal Korean Medicine
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• v.44 no.6
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• pp.1197-1211
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• 2023

Objectives: This study presents evidence by analyzing the research trends in acupuncture treatment for cluster headache in the last 10 years. Methods: Randomized controlled trials (RCTs) about acupuncture for cluster headache were searched from the China National Knowledge Infrastructure, PubMed, Cochrane Library, Oriental Medicine Advanced Searching Integrated System, ScienceON, Korean Studies Information Service System, and Research Information Sharing Service. The search terms were the combinations of "cluster headache", "acupuncture", and "needle therapy", and the articles were restricted to those published between 2013 and 2023. Only RCTs were selected. The risk of bias (RoB) was assessed according to the revised Cochrane RoB2 criteria. Results: Six RCTs were selected and analyzed in this review. All selected studies were conducted in China. All RCTs comprised 628 participants. Manual acupuncture was used in all studies. Acupuncture targeting the sphenopalatine ganglion was performed in two papers published after 2020. ST8, Ex-HIN3, and GB14 were the most frequently used acupoints in acupuncture treatment. The most commonly used indicators for evaluation were headache attack frequency, clinical efficacy, and the visual analog scale. In each study, adding acupuncture treatment to conventional therapy had significant effects in relieving the symptoms of cluster headaches. Conclusion: The results suggest that acupuncture is an effective treatment for cluster headache. To ensure objective evidence for the effectiveness of acupuncture treatment in cluster headache, it is important to continue large-scale case reports and RCTs.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2004.04a
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• pp.389-395
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• 2004

We consider the interface between the parallel distributed memory multifrontal solver and the finite element method. We give in detail the requirement and the data structure of parallel FEM interface which includes the element data and the node array. The full procedures of solving a large scale structural problem are assumed to have pre-post processors, of which algorithm is not considered in this paper. The main advantage of implementing the parallel FEM interface is shown up in the case that we use a distributed memory system with a large number of processors to solve a very large scale problem. The memory efficiency and the performance effect are examined by analyzing some examples on the Pegasus cluster system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.5972-5989
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• 2019

With the onset of the big data age, data is growing exponentially, and the issue of how to optimize large-scale data processing is especially significant. Large-scale global optimization (LSGO) is a research topic with great interest in academia and industry. Spark is a popular cloud computing framework that can cluster large-scale data, and it can effectively support the functions of iterative calculation through resilient distributed datasets (RDD). In this paper, we propose a hybrid mechanism of particle swarm optimization (PSO) and differential evolution (DE) algorithms based on Spark (SparkPSODE). The SparkPSODE algorithm is a parallel algorithm, in which the RDD and island models are employed. The island model is used to divide the global population into several subpopulations, which are applied to reduce the computational time by corresponding to RDD's partitions. To preserve population diversity and avoid premature convergence, the evolutionary strategy of DE is integrated into SparkPSODE. Finally, SparkPSODE is conducted on a set of benchmark problems on LSGO and show that, in comparison with several algorithms, the proposed SparkPSODE algorithm obtains better optimization performance through experimental results.

• International journal of advanced smart convergence
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• v.10 no.4
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• pp.278-288
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• 2021

Recently, IoT systems are cloud-based, so that continuous and large amounts of data collected from sensor nodes are processed in the data server through the cloud. However, in the centralized configuration of large-scale cloud computing, computational processing must be performed at a physical location where data collection and processing take place, and the need for edge computers to reduce the network load of the cloud system is gradually expanding. In this paper, a cluster system consisting of 6 inexpensive Raspberry Pi boards was constructed to perform fast data processing. And we propose "Kubernetes cluster system(KCS)" for processing large data collection and analysis by model distribution and data pipeline method. To compare the performance of this study, an ensemble model of deep learning was built, and the accuracy, processing performance, and processing time through the proposed KCS system and model distribution were compared and analyzed. As a result, the ensemble model was excellent in accuracy, but the KCS implemented as a data pipeline proved to be superior in processing speed..

• Journal of Industrial Technology
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• v.29 no.A
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• pp.123-126
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• 2009

Cluster-based servers are utilized for large scale streaming services. These servers show good performance under fair resource usage balance policies between backend nodes. In this paper, cluster-based VOD servers are implemented based on WMI functions. Based on these functions, the information of resource usages in backend nodes can be exchanged and also the working states of cluster nodes can be monitored periodically. In experiences, we find motivations to design an effective load balancing policy. In addition, to provide reliable streaming services, the method to detect a partial failure in cluster-based servers is proposed.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06a
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• pp.345-346
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• 2010

• The Bulletin of The Korean Astronomical Society
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• v.43 no.2
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• pp.35.1-35.1
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• 2018

Bars are commonly found in disk galaxies. However, how bars form is yet unclear. There are two common pictures for the bar formation mechanism. Bars form through a physical process inherent in galaxies, or through and external process like galaxy-galaxy interaction. In this paper, we present the observational evidence that bars can form from another channel, namely a cluster-cluster interaction. We examined 105 galaxy clusters at 0.015