• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.101-108
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• 2023

Although the proportion of coal-fired power generation is decreasing, efficient operating technology is needed to continuously invest in facilities and reduce maintenance costs until it is abolished. Boilers, one of the main facilities of power plants, operate for a long time in harsh environments of high temperature and high pressure. In addition, damage due to deterioration is likely to occur depending on the fuel and tube material used. It is very important to judge soundness because damage caused by deterioration adversely affects facility operation. Previously, replication method was used to analyze the progress of deterioration. In the replication method, pre-treatment such as chemical treatment is performed on the boiler tube in the field, the area is reproduced by attaching a film, and the replicated film is determined by an expert in the laboratory with an expensive microscope. However, this method involves substantial costs and time requirements, as well as the possibility of human errors. To address these issues, we developed a mobile health assessment system in this research. Since it is detachable and takes images in real time, this system enables swift evaluations across a broad range and facilitates the assessment of preprocessing quality. In addition, it was intended to reduce existing human mistakes by developing a degradation classification algorithm using the merger cluster method.

• Journal of the Ergonomics Society of Korea
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• v.32 no.3
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• pp.237-243
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• 2013

Objective: The present study developed an estimation method of boundary zone representative humanoids(hereafter, EBZ method) using descriptive statistics on the design target population. Background: The boundary zone method(hereafter, BZ method) generates representative humanoids at a boundary zone that statistically accommodates a designated percent of the design target population; however, the BZ method has a practical limitation because it requires a large scale anthropometric database on the design target population. Method: The EBZ method developed in the present study consisted of 3 steps. In the first step, the boundary zone of accommodating a designated percent(e.g., 90%) is formed under the assumption of normal distributions for anthropometric sizes. In the second step, cases that fall within the boundary zone are estimated using descriptive statistics(mean, standard deviation, and covariance) on the design target population. In the last step, K-mean cluster analysis is conducted for the cases, and representative humanoids are selected from each of clusters. Results: Evaluation results showed that mean accommodation percent of the EBZ method was 90.9%(range: 90.8~91.1%) which is similar to the target percent(90%). In addition, standard deviation of accommodation percent for 100 repetitions was 0.1%. Lastly, the number of representative humanoids generated by the EBZ method(n = 20) was similar to the BZ method(n = 16). Conclusion: The EBZ method can generate representative humanoids which accommodate a designated percent of the design target population using descriptive statistics. Application: The EBZ method can be utilized in the generation of humanoids for ergonomic design and evaluation of products when the large scale anthropometric database on the design target population is not existed.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.6 s.312
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• pp.20-27
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• 2006

Multi-level thresholding is a method that is widely used in image segmentation. However most of the existing methods are not suited to be directly used in applicable fields and moreover expanded until a step of image segmentation. This paper proposes region-based multi-level thresholding as an image segmentation method. At first we classify pixels of each color channel to two clusters by using EWFCM(Entropy-based Weighted Fuzzy C-Means) algorithm that is an improved FCM algorithm with spatial information between pixels. To obtain better segmentation results, a reduction of clusters is then performed by a region-based reclassification step based on a similarity between regions existing in a cluster and the other clusters. The clusters are created using the classification information of pixels according to color channel. We finally perform a region merging by Bayesian algorithm based on Kullback-Leibler distance between a region and the neighboring regions as a post-processing method as many regions still exist in image. Experiments show that region-based multi-level thresholding is superior to cluster-, pixel-based multi-level thresholding, and the existing mettled. And much better segmentation results are obtained by the post-processing method.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.4
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• pp.267-273
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• 2011

In this paper, we proposed the method to detect brain tumor region in MR images. Our method is composed of 3 parts, detection of tumor slice, detection of tumor region and tumor boundary detection. In the tumor slice detection step, a slice which contains tumor regions is distinguished using symmetric analysis in 3D brain volume. The tumor region detection step is the process to segment the tumor region in the slice distinguished as a tumor slice. And tumor region is finally detected, using spatial feature and symmetric analysis based on the cluster information. The process for detecting tumor slice and tumor region have advantages which are robust for noise and requires less computational time, using the knowledge of the brain tumor and cluster-based on symmetric analysis. And we use the level set method with fast marching algorithm to detect the tumor boundary. It is performed to find the tumor boundary for all other slices using the initial seeds derived from the previous or later slice until the tumor region is vanished. It requires less computational time because every procedure is not performed for all slices.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.6
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• pp.70-77
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• 2008

This paper proposes a new text candidate region detection method that uses genetic algorithm based on information of the segmented regions. In image segmentation, a classification of the pixels at each color channel and a reclassification of the region-unit for reducing inhomogeneous clusters are performed. EWFCM(Entropy-based Weighted C-Means) algorithm to classify the pixels at each color channel is an improved FCM algorithm added with spatial information, and therefore it removes the meaningless regions like noise. A region-based reclassification based on a similarity between each segmented region of the most inhomogeneous cluster and the other clusters reduces the inhomogeneous clusters more efficiently than pixel- and cluster-based reclassifications. And detecting text candidate regions is performed by genetic algorithm based on energy and variance of the directional edge components, the number, and a size of the segmented regions. The region information-based detection method can singles out semantic text candidate regions more accurately than pixel-based detection method and the detection results will be more useful in recognizing the text regions hereafter. Experiments showed the results of the segmentation and the detection. And it confirmed that the proposed method was superior to the existing methods.

• The Korean Journal of Applied Statistics
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• v.33 no.6
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• pp.791-804
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• 2020

Residential electricity consumption can be predicted more accurately by utilizing the realtime household electricity consumption reference that can be collected by the AMI as the ICT developed under the smart grid circumstance. This paper studied the model that predicts residential power load using the ARIMA, TBATS, NNAR model based on the data of hour unit amount of household electricity consumption, and unlike forecasting the consumption of the whole households at once, it computed the anticipated amount of the electricity consumption by aggregating the predictive value of each established model of cluster that was collected by the households which show the similiar load profile. Especially, as the typical time series data, the electricity consumption data chose the clustering analysis method that is appropriate to the time series data. Therefore, Dynamic Time Warping and Periodogram based method is used in this paper. By the result, forecasting the residential elecrtricity consumption by clustering the similiar household showed better performance than forecasting at once and in summertime, NNAR model performed best, and in wintertime, it was TBATS model. Lastly, clustering method showed most improvements in forecasting capability when the DTW method that was manifested the difference between the patterns of each cluster was used.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.11-19
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• 2021

In this paper, we propose a non-orthogonal multiple access (NOMA) method based on channel alignment to simultaneously transmit multiple unicast and multicast streams in frequency-efficient manner. In this method, all receivers in a multicast cluster use the receive beamforming vectors that align their channels, and the base station uses the aligned channel information to design the transmit beamforming vectors that eliminate interference between multicast clusters. Using the effective receive channel information combined with the transmit beamforming vectors, unicast receivers design their own receive beamforming vectors that eliminate interference between unicast receivers. Since the proposed method effectively eliminates the interference, it achieves a higher sum rate than the existing orthogonal multiple access (OMA) method in high SNR regions. In addition, we present a hybrid method that exploits the benefits of the proposed NOMA method and the existing OMA method. Depending on the channel state, the hybrid method adaptively employs the existing OMA method, which improves the received signal power, in low SNR regions and the proposed NOMA method, which effectively eliminates the interference, in high SNR regions, thereby achieving a good sum rate over the entire SNR region.

• Journal of Korean Academy of Nursing Administration
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• v.9 no.4
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• pp.599-613
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• 2003

Purpose: The ultimate aim of the inquiry was to discover the essence of nurse's experience and promote understanding. Method : Guided by Colaizzi's method - 1. Description of the phenomena of interest by the reader. 2. collection of subject's description of the phenomena, 3. Reading all the subject's descriptions of the phenomenon, 4. Returning the original transcripts and extracting significant statements, 5. Trying to spell out the meaning of each significant statements, 6. Organizing the aggregate formalized meanings into clusters of themes. 7. Writing an exhaustive description, 8. Returning to the subjects for validation of the description, 9. If new data fare revealed during the validations incorporating them into an exhaustive description. The participants in this study were eight are nurses working for clinics. This strategies for data collecting were needed : deep face to face interview. Results : 6 cluster of themes are : 1. the heavy pressure, 2. the pride and the royal summons, 3. the powerlessness, 4. the hope, 5. the tiresome. 6. the distressed feeling. Conclusion : The results of the this study would help us to understand nurses in clinics, make direction for nursing education, and identify need for continuing inquiry.

• Bulletin of the Korean Chemical Society
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• v.34 no.9
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• pp.2635-2639
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• 2013

The rapid and accurate identification of biological agents is a critical step in the case of bio-terror and biological warfare attacks. Recently, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry has been widely used for the identification of microorganisms. In this study, we describe a method for the rapid and accurate discrimination of Bacillus anthracis spores using MALDI-TOF MS. Our direct in-situ analysis of MALDI-TOF MS does not involve subsequent high-resolution mass analyses and sample preparation steps. This method allowed the detection of species-specific biomarkers from each Bacillus spores. Especially, B. anthracis spores had specific biomarker peaks at 2503, 3089, 3376, 6684, 6698, 6753, and 6840 m/z. Cluster and PCA analyses of the mass spectra of Bacillus spores revealed distinctively separated clusters and within-groups similarity. Therefore, we believe that this method is effective in the real-time identification of biological warfare agents such as B. anthracis as well as other microorganisms in the field.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.25-29
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• 2001

There are so many researches about the search method for the most compatible dispatching rule to a manufacturing system state. Most of researches select the dispatching rule using simulation results. This paper touches upon two research topics: the clustering method for manufacturing system states using simulation, and the search method for the most compatible dispatching rule to a manufacturing system state. The manufacturing system state variables are given to ART II neural network as input. The ART II neural network is trained to cluster the system state. After being trained, the ART II neural network classifies any system state as one state of some clustered states. The simulation results using clustered system state information and those of various dispatching rules are compared and the most compatible dispatching rule to the system state is defined. Finally there are made two knowledge bases. The simulation experiments are given to compare the proposed methods with other scheduling methods. The result shows the superiority of the proposed knowledge base.