• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.3
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• pp.265-274
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• 2022

This paper proposes a machine learning-based screening algorithm to build the retired battery pack of the energy storage system. The proposed algorithm creates the dataset of various performance parameters of the retired battery, and this dataset is preprocessed through a principal component analysis to reduce the overfitting problem. The retried batteries with a large deviation are excluded in the dataset through a density-based spatial clustering of applications with noise, and the K-means clustering method is formulated to select the group of the retired batteries to satisfy the deviation requirement conditions. The performance of the proposed algorithm is verified based on NASA and Oxford datasets.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.248-249
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• 2015

Clustering is one of the most important unsupervised learning methods that clusters data into homogeneous groups. However, cluster centers tend leaning to high density clusters because clustering is based on the distances between data points and cluster centers. In this paper, a modified clustering method forcing cluster centers to be apart by introducing a center-scattering term in the Fuzzy C-Means objective function is introduced. The proposed method converges more to real centers with small number of iterations compared to the original one. All the strengths can be verified with experimental results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.2
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• pp.326-354
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• 2014

In this paper, a new green clustering algorithm is proposed to be as a first approach in the framework of an energy efficient strategy for centralized enterprise high-density WLANs. Traditionally, in order to maintain the network coverage, all the APs within the WLAN have to be powered-on. Nevertheless, the new algorithm can power-off a large proportion of APs while the coverage is maintained as its always-on counterpart. The two main components of the new approach are the faster procedure based on K-means and the more accurate procedure based on Evolutionary Algorithm (EA), respectively. The two procedures are processes in parallel for different designed requirements and there is information interaction in between. In order to implement the new algorithm, EA is applied to handle the optimization of multiple objectives. Moreover, we adapt the method for selection and recombination, and then introduce a new operator for mutation. This paper also presents simulations in scenarios modeled with ray-tracing method and FDTD technique, and the results show that about 67% to 90% of energy consumption can be saved while it is able to maintain the original network coverage during periods when few users are online or the traffic load is low.

• Communications for Statistical Applications and Methods
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• v.31 no.3
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• pp.349-363
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• 2024

Compositional data refers to data where the sum of the values of the components is a constant, hence the sample space is defined as a simplex making it impossible to apply statistical methods developed in the usual Euclidean vector space. A natural approach to overcome this restriction is to consider an appropriate transformation which moves the sample space onto the Euclidean space, and log-ratio typed transformations, such as the additive log-ratio (ALR), the centered log-ratio (CLR) and the isometric log-ratio (ILR) transformations, have been mostly conducted. However, in scenarios with sparsity, where certain components take on exact zero values, these log-ratio type transformations may not be effective. In this work, we mainly suggest an alternative transformation, that is the square-root transformation which moves the original sample space onto the directional space. We compare the square-root transformation with the log-ratio typed transformation by the simulation study and the real data example. In the real data example, we applied both types of transformations to the USG% data obtained from NBA, and used a density based clustering method, DBSCAN (density-based spatial clustering of applications with noise), to show the result.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.83-89
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• 2023

The clustering technique using RF fingerprint extracts the characteristic signature of the transmitters which are embedded in the transmission waveforms. The output of the RF-Fingerprint feature extraction algorithm for clustering identical DMR(Digital Mobile Radios) is a high-dimensional feature, typically consisting of 512 or more dimensions. While such high-dimensional features may be effective for the classifiers, they are not suitable to be used as inputs for the clustering algorithms. Therefore, this paper proposes a dimension reduction algorithm that effectively reduces the dimensionality of the multidimensional RF-Fingerprint features while maintaining the fingerprinting characteristics of the DMRs. Additionally, it proposes a clustering algorithm that can effectively cluster the reduced dimensions. The proposed clustering algorithm reduces the multi-dimensional RF-Fingerprint features using t-SNE, based on KL Divergence, and performs clustering using Density Peaks Clustering (DPC). The performance analysis of the DMR clustering algorithm uses a dataset of 3000 samples collected from 10 Motorola XiR and 10 Wintech N-Series DMRs. The results of the RF-Fingerprinting-based clustering algorithm showed the formation of 20 clusters, and all performance metrics including Homogeneity, Completeness, and V-measure, demonstrated a performance of 99.4%.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2917-2932
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• 2018

Many researches are accomplished as a result of the efforts of developing the production predicting model to solve the supply imbalance of onions which are vegetables very closely related to Korean food. But considering the possibility of storing onions, it is very difficult to solve the supply imbalance of onions only with predicting the production. So, this paper's purpose is trying to build a sentiment dictionary to predict the price of onions by using the internet articles which include the informations about the production of onions and various factors of the price, and these articles are very easy to access on our daily lives. Articles about onions are from 2012 to 2016, using TF-IDF for comparing with four kinds of TF-IDFs through the documents classification of wholesale prices of onions. As a result of classifying the positive/negative words for price by k-means clustering, DBSCAN (density based spatial cluster application with noise) clustering, GMM (Gaussian mixture model) clustering which are partitional clustering, GMM clustering is composed with three meaningful dictionaries. To compare the reasonability of these built dictionary, applying classified articles about the rise and drop of the price on logistic regression, and it shows 85.7% accuracy.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.5
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• pp.1131-1141
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• 2015

In this paper, we proposed a new approach of machine learning based method for detecting game-bots from normal players in MMORPG by inspecting the player's action log data especially in-game money increasing/decreasing event log data. DBSCAN (Density Based Spatial Clustering of Applications with Noise), an one of density based clustering algorithms, is used to extract the attributes of spatial characteristics of each players such as a number of clusters, a ratio of core points, member points and noise points. Most of all, even game-bot developers know principles of this detection system, they cannot avoid the system because moving a wide area to hunt the monster is very inefficient and unproductive. As the result, game-bots show definite differences from normal players in spatial characteristics such as very low ratio, less than 5%, of noise points while normal player's ratio of noise points is high. In experiments on real action log data of MMORPG, our game-bot detection system shows a good performance with high game-bot detection accuracy.

• Journal of Internet Computing and Services
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• v.13 no.5
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• pp.1-8
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• 2012

In this paper, we propose a new clustering combination based on numerical data driven for rule generation mechanism. In large and complicated space, a clustering method can obtain limited performance results. To overcome the single clustering method problem, hybrid combined methods can solve problem to divided simple cluster estimation. Fundamental structure of the proposed method is combined by mountain clustering and modified Chen clustering to extract detail cluster information in complicated data distribution of non-parametric space. It has automatic rule generation ability with advanced density based operation when intelligent systems including neural networks and fuzzy inference systems can be generated by clustering results. Also, results of the mechanism will be served to information of decision support system to infer the useful knowledge. It can extend to healthcare and medical decision support system to help experts or specialists. We show and explain the usefulness of the proposed method using simulation and results.

• The Journal of the Korea Contents Association
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• v.6 no.12
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• pp.113-120
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• 2006

This paper proposes DBSCAN-SI that is an algorithm for clustering with influences of spatial objects. DBSCAN-SI that is extended from existing DBSCAN and DBSCAN-W converts from non-spatial properties to the influences of spatial objects during the spatial clustering. It increases probability of inclusion to the cluster according to the higher the influences that is affected by the properties used in clustering and executes the clustering not only respect the spatial distances, but also volume of influences. For the perspective of specific property-centered, the clustering technique proposed in this paper can makeup the disadvantage of existing algorithms that exclude the objects in spite of high influences from cluster by means of being scarcely close objects around the cluster.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.1 no.3
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• pp.69-76
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• 2008

This paper concerns Fuzzy Radial Basis Function Neural Network (FRBFNN) and automatic rule generation of extraction of the FRBFNN by means of mountain clustering. In the proposed network, the membership functions of the premise part of fuzzy rules do not assume any explicit functional forms such as Gaussian, ellipsoidal, triangular, etc., so its resulting fitness values (degree of membership) directly rely on the computation of the relevant distance between data points. Also, we consider high-order polynomial as the consequent part of fuzzy rules which represent input-output characteristic of sup-space. The number of clusters and the centers of clusters are automatically generated by using mountain clustering method based on the density of data. The centers of cluster which are obtained by using mountain clustering are used to determine a degree of membership and weighted least square estimator (WLSE) is adopted to estimate the coefficients of the consequent polynomial of fuzzy rules. The effectiveness of the proposed model have been investigated and analyzed in detail for the representative nonlinear function.