• Journal of the Institute of Convergence Signal Processing
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• v.6 no.1
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• pp.23-26
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• 2005

This paper describes the algorithm for deciding the status of the operating machines in the power plants. It is very important to decide whether the status of the operating machines is good or not in the industry to protect the accidents of machines and improve the operation efficiency of the plants. There are two steps to analyze the status of the running machines. First, we extract the features from the input original data. Second, we classify those features into normal/abnormal condition of the machines using the wavelet transform and the input RMS vector through the K-means algorithm. In this paper we developed the algorithm to detect the fault operation using the K-means method from the sound of the operating machines.

• The Journal of the Convergence on Culture Technology
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• v.7 no.2
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• pp.345-350
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• 2021

The maintenance of sensor networks, installed in a wide area has been an issue for a long time. In order to solve this problem, studies to supply energy to a sensor network using a robot has been carried out by several researchers. In this study, for a sensor network consisting of power nodes supplied with energy by multiple robots and sensor nodes around them, we propose a method of allocating a work area using a modified k-means algorithm so that the robots move the minimum distance. Through the simulation study using the energy transfer rate of the robot as a variable, it is shown that nodes of each allocated area can maintain survival, and the validity of the proposed modified k-means algorithm is verified.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.177-182
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• 2022

Technology is progressing with every passing day and the enormous usage of electricity is becoming a necessity. One of the techniques to enjoy the assistances in a smart home is the efficiency to manage the electric energy. When electric energy is managed in an appropriate way, it drastically saves sufficient power even to be spent during hard time as when hit by natural calamities. To accomplish this, prediction of energy consumption plays a very important role. This proposed prediction model Coherent Weighted K-Means Clustering ARIMA (CWKMCA) enhances the weighted k-means clustering technique by adding weights to the cluster points. Forecasting is done using the ARIMA model based on the centroid of the clusters produced. The dataset for this proposed work is taken from the Pecan Project in Texas, USA. The level of accuracy of this model is compared with the traditional ARIMA model and the Weighted K-Means Clustering ARIMA Model. When predicting,errors such as RMSE, MAPE, AIC and AICC are analysed, the results of this suggested work reveal lower values than the ARIMA and Weighted K-Means Clustering ARIMA models. This model also has a greater loglikelihood, demonstrating that this model outperforms the ARIMA model for time series forecasting.

• The Journal of Korean Academy of Prosthodontics
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• v.26 no.1
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• pp.133-151
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• 1988

The objectives of this study were to compare the position of centric relation guided by means of leaf gauges, bilateral manipulation, chin-point guidance with Lucia jig and self-guided method and the reproducibility, respectively. A Veri-check (Denar Co., Anaheim, California) was employed for examining, and the displacement of position and reproducibility were verified. The following results were obtained. 1. On the sagittal plane, the centric relation guided by means of leaf gauges showed greater posterior displacement than that by means of bilateral manipulation and less than that by means of chin-point guidance with Lucia jig, and self-guided centric relation showed least posterior displacement. The centric relation guided by means of bilateral manipulation showed greater superior displacement than that by means of chin-point guidance with Lucia jig and less than that by means of leaf gauges and self-guided centric relation showed least superior displacement. 2. On the horizontal plane, the centric relation guided by means of chin-point guidance with Lucia jig showed greater posterior displacement than that of bilateral manipulation and less than that leaf gauges, however the self-guided centric relation showed slightly anterior displacement. 3. The anteroposterior displacement measured on sagittal plane and horizontal plane were highly correlated. (p<0.05). 4. The reproducibility of centric relation guided by means of leaf gauges, bilateral manipulation and chin-point guidance with Lucia jig were similar and more reproducible than self-guided centric relation.

• Proceedings of the Korean Society of Computer Information Conference
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• 2013.01a
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• pp.301-302
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• 2013

본 논문에서는 초음파 영상에서 퍼지 C-Means 클러스터링을 이용한 양자화 기법을 제안한다. 제안된 방법은 초음파 영상에서 나타난 명암도를 이용하여 n개의 그룹으로 클러스터링한다. 그리고 각 클러스터의 중심 값을 기준으로 정렬한 뒤, 각 그룹에 지정된 색상을 요부 초음파 영상에서 나타낸다. 본 논문에서 제안하는 기법을 적용한 요부 초음파 영상과 일반적으로 자주 이용되는 히스토그램 기반 양자화 기법을 적용한 요부 초음파 영상을 비교하였을 때, 본 논문에서 제안하는 퍼지 C-Means 클러스터링을 이용한 양자화를 적용한 영상이 근육 내의 지방을 분석하는데 효과적인 것을 확인할 수 있었다.

• Communications for Statistical Applications and Methods
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• v.2 no.1
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• pp.176-184
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• 1995

In an exponential distribution, the properties of the interior and exterior trimmed means will be introduced, and reliability estimators using the two trimmed means will be compared with the UMVUE of reliability function through simulations.

• Communications of the Korean Mathematical Society
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• v.24 no.4
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• pp.553-559
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• 2009

In the paper, some new inequalities for certain bivariate means are obtained, which extend some known results.

• Journal of the Korean Data and Information Science Society
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• v.27 no.3
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• pp.609-619
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• 2016

RHadoop is a collection of R packages that allow users to manage and analyze data with Hadoop. In this paper, we implement K-Means algorithm based on MapReduce framework with RHadoop to make the clustering method applicable to large scale data. The main idea introduces a combiner as a function of our map output to decrease the amount of data needed to be processed by reducers. We showed that our K-Means algorithm using RHadoop with combiner was faster than regular algorithm without combiner as the size of data set increases. We also implemented Elbow method with MapReduce for finding the optimum number of clusters for K-Means clustering on large dataset. Comparison with our MapReduce implementation of Elbow method and classical kmeans() in R with small data showed similar results.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.6
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• pp.1223-1229
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• 2009

The performance of Radial Basis Function Neural Networks depends on setting up the Radial Basis Functions over the input space which are the important design procedure of Radial Basis Function Neural Networks. The existing method to initialize the location of the radial basis functions over the input space is to use the conditional fuzzy C-means clustering. However, the researchers which are interested in the conditional fuzzy C-means clustering cannot get as good modeling performance as they expect because the conditional fuzzy C-means clustering cannot project the information which is extracted over the output space into the input space. To compensate the above mentioned drawback of the conditional fuzzy C-means clustering, we apply a fuzzy K-nearest neighbors approach to project the auxiliary information defined over the output space into the input space without lose of the information.

• Journal of Internet of Things and Convergence
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• v.3 no.2
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• pp.1-5
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• 2017

As CCTV technology develops, it is used in various fields. Currently, we want to know about CCTV operation in detail. In addition, CCTV in many fields is causing problems in operation. We plan to design a new system to solve the problem. In this paper, we analyze data using K-means so that CCTV can be operated efficiently, add new technology and function to existing system to increase image technology and operate efficiently, Technology. In addition, we will design a new system for CCTV technology using k-means so that the CCTV can be efficiently operated in the center, and propose the problem to solve the problem.