• KIISE Transactions on Computing Practices
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• v.23 no.8
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• pp.481-486
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• 2017

There are many scientists who want to perform experiments in a cloud environment, and pay-per-use services allow scientists to pay only for cloud services that they need. However, it is difficult for scientists to select a suitable set of resources since those resources are comprised of various characteristics. Therefore, classification is needed to support the effective utilization of cloud resources. Thus, a dynamic resource clustering method is needed to reflect the characteristics of the application that scientists want to execute. This paper proposes a resource clustering analysis method that takes into account the characteristics of an application in a hybrid cloud environment. The resource clustering analysis applies a Self-Organizing Map and K-means algorithm to dynamically cluster similar resources. The results of the experiment indicate that the proposed method can classify a similar resource cluster by reflecting the application characteristics.

• Journal of Intelligence and Information Systems
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• v.8 no.2
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• pp.1-13
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• 2002

In this paper, we propose an object tracking system which can be convinced of moving area shaped on objects through color sequential images, decided moving directions of foot messengers or vehicles of image sequences. In static camera, we suggests a new evaluating method extracting co-occurrence matrix with feature vectors of RGB after analyzing and blocking difference images, which is accessed to field of camera view for motion. They are energy, entropy, contrast, maximum probability, inverse difference moment, and correlation of RGB color vectors. we describe how to analyze and compute corresponding relations of objects between adjacent frames. In the clustering, we apply an algorithm of FCM(fuzzy c means) to analyze matching and clustering problems of adjacent frames of the featured vectors, energy and entropy, gotten from previous phase. In the matching phase, we also propose a method to know correspondence relation that can track motion each objects by clustering with similar area, compute object centers and cluster around them in case of same objects based on membership function of motion area of adjacent frames.

• Journal of IKEEE
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• v.23 no.3
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• pp.910-916
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• 2019

Non-local similarity of natural images is one of highly exploited features in various applications dealing with images. Unique edges, texture, and pattern of the images are frequently repeated over the entire image. Once the similar image blocks are classified into a cluster, representative features of the image blocks can be extracted from the cluster. The bigger the size of the cluster is the better the additive white noise can be separated. Denoising is one of major research topics in the image processing field suppressing the additive noise. In this paper, a denoising algorithm is proposed which first clusters the noisy image blocks based on similarity, extracts the feature of the cluster, and finally recovers the original image. Performance experiments with several images under various noise strengths show that the proposed algorithm recovers the details of the image such as edges, texture, and patterns while outperforming the previous methods in terms of PSNR in removing the additive Gaussian noise.

• International Journal of Control, Automation, and Systems
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• v.4 no.6
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• pp.705-713
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• 2006

For the analysis of multiunit extracellular neural signals as multiple spike trains, neural spike sorting is essential. Existing algorithms for the spike sorting have been unsatisfactory when the signal-to-noise ratio(SNR) is low, especially for implementation of fully-automated systems. We present a novel method that shows satisfactory performance even under low SNR, and compare its performance with a recent method based on principal component analysis(PCA) and fuzzy c-means(FCM) clustering algorithm. Our system consists of a spike detector that shows high performance under low SNR, a feature extractor that utilizes projection pursuit based on negentropy maximization, and an unsupervised classifier based on Gaussian mixture model. It is shown that the proposed feature extractor gives better performance compared to the PCA, and the proposed combination of spike detector, feature extraction, and unsupervised classification yields much better performance than the PCA-FCM, in that the realization of fully-automated unsupervised spike sorting becomes more feasible.

• Journal of Digital Contents Society
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• v.19 no.1
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• pp.27-33
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• 2018

In this study, we analyze the big data of visitors who are looking for a sports stadium in marketing field and conduct research to provide customized marketing service to consumers. For this purpose, we intend to derive a similar visitor group by using the K-means clustering method. Also, we will use the K-nearest neighbors method to predict the store of interest for new visitors. As a result of the experiment, it was possible to provide a marketing service suitable for each group attribute by deriving a group of similar visitors through the above two algorithms, and it was possible to recommend products and events for new visitors.

• Proceedings of the KIEE Conference
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• 2006.10d
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• pp.34-36
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• 2006

In this paper, we will treat the diagnosis problem to accurately determine fault types. The judgement of fault types is accomplished by observing the cluster newly formed with faults and clustering the input current waveforms to intrinsically show the conditions with the dignet that is a clustering algorithm. The types of input current waveforms are, however, constrained during normal operation, though it considers the load character. In case of faults. new clusters are generated outside the clusters. which appear during normal operation, because the input current waveforms of the induction motor are generated by the type which is not observed in case of faults. The diagnosis about the types of faults is essential to building a fault tree about the induction motor, and it removes the causes of the faults using a fuzzy logic. We, first, constitute a fault tree, which connects with the parts and the entire system of the induction motor, and investigate fault modes which can be generated from the fault tree and the relationship of the cause and the effect of each part (of the motor). Also, we distinguish the faults of each part by means of inducing the said of fuzzy relation equations encapsulating the relationship of the fault modes and each part.

• Journal of the Korea Society of Computer and Information
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• v.19 no.3
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• pp.17-24
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• 2014

In this paper, we propose a mobile application categorization method using word cluster information. Because the mobile application description can be shortly written, the proposed method utilizes the word cluster seeds as well as the words in the mobile application description, as categorization features. For the fragmented categories of the mobile applications, the proposed method generates the word clusters by applying the frequency of word occurrence per category to K-means clustering algorithm. Since the mobile application description can include some paragraphs unrelated to the categorization, such as installation specifications, the proposed method uses some word clusters useful for the categorization. Experiments show that the proposed method improves the recall (5.65%) by using the word cluster information.

• International Journal of Control, Automation, and Systems
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• v.1 no.2
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• pp.194-202
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• 2003

In this paper, we introduce a category of Multi-FNN (Fuzzy-Neural Networks) models, analyze the underlying architectures and propose a comprehensive identification framework. The proposed Multi-FNNs dwell on a concept of fuzzy rule-based FNNs based on HCM clustering and evolutionary fuzzy granulation, and exploit linear inference being treated as a generic inference mechanism. By this nature, this FNN model is geared toward capturing relationships between information granules known as fuzzy sets. The form of the information granules themselves (in particular their distribution and a type of membership function) becomes an important design feature of the FNN model contributing to its structural as well as parametric optimization. The identification environment uses clustering techniques (Hard C - Means, HCM) and exploits genetic optimization as a vehicle of global optimization. The global optimization is augmented by more refined gradient-based learning mechanisms such as standard back-propagation. The HCM algorithm, whose role is to carry out preprocessing of the process data for system modeling, is utilized to determine the structure of Multi-FNNs. The detailed parameters of the Multi-FNN (such as apexes of membership functions, learning rates and momentum coefficients) are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. To evaluate the performance of the proposed model, two numeric data sets are experimented with. One is the numerical data coming from a description of a certain nonlinear function and the other is NOx emission process data from a gas turbine power plant.

• Journal of Practical Engineering Education
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• v.14 no.3
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• pp.565-572
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• 2022

Card sorting is a useful data collection method for understanding users' perceptions of relationships between items. In general, card sorting is an intuitive and cost-effective technique that is very useful for user research and evaluation. In this study, the core competencies of each field were used as competency cards used in the next stage of card sorting for course development, and the clustering results were derived by applying the K-means algorithm to cluster the results. As a result of card sorting, competency clustering for core competencies for each occupation in each field was verified based on Participant-Centric Analysis (PCA). For the number of core competency cards for each occupation, the number of participants who agreed appropriately for clustering and the degree of card similarity were derived compared to the number of sorting participants.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.11
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• pp.1199-1206
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• 2012

Recently, technologies to help the elderly or disabled people who have difficulty in walking are being developed. In order to develop these technologies, it is necessary to construct a system that gathers the gait data of people and analysis of these data is also important. In this research, we constructed the development of sensor system which consists of pressure sensor, three-axis accelerometer and two-axis gyro sensor. We used k-means clustering algorithm to classify the data for characterization, and then calculated the symmetry index with histogram which was produced from each cluster. We collected gait data from sensors attached on two subjects. The experiment was conducted for two kinds of gait status. One is walking with normal gait; the other is walking with abnormal gait (abnormal gait means that the subject walks by dragging the right leg intentionally). With the result from the analysis of acceleration component, we were able to confirm that the analysis technique of this data could be used to determine gait symmetry. In addition, by adding gyro components in the analysis, we could find that the symmetry index was appropriate to express symmetry better.