• The Korean Journal of Applied Statistics
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• v.33 no.6
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• pp.683-696
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• 2020

The aim of this research is to find an optimal gene set that provides highly accurate multi-class classification with a minimum number of genes. A two-stage procedure is proposed: Based on minimum redundancy and maximum relevance (mRMR) framework, several statistics to rank differential expression genes and K-means clustering to reduce redundancy between genes are used for data filtering procedure. And a particle swarm optimization is modified to select a small subset of informative genes. Two well known multi-class microarray data sets, ALL and SRBCT, are analyzed to indicate the effectiveness of this hybrid method.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.3
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• pp.433-442
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• 2023

The purpose of this study is to reveal the Korean learners' usage pattern of '의', the genitive particle, according to semantic classification, so that it can be referred to in determining the contents and methods of related education. The method of this study adopts a quantitative analysis using learners corpus established by National Institute of Korean Language. As a result of the analysis, as proficiency increases, the overall frequency of '의' increases and the number of meaning senses used increases. However, the frequency of errors also increases with it. As for the usage pattern of each sense, the meaning of 'ownership, belonging' is the most frequent, and followed by 'acting entity', 'kinship, social relations', and 'relationship(area)'. In conclusion, the meanings of 'acting subjects' and 'relationships(area) need to be supplemented with explicit education. Other meanings need to be discussed, and decisions should be made in consideration of learning purpose and proficiency.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2000.06a
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• pp.54-61
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• 2000

A method and results of classification of 4 types metallic wear debris were presented by using their color features. The color image of wear debris was used (or the initial data, and the color properties of the debris were specified by HSI color model. Particle was characterized by a set of statistical features derived from the distribution of HSI color model components. The initial feature set was optimized by a principal component analysis, and multidimensional scaling procedure was used for the definition of classification plane. It was found that five features, which include mean values of H and S, median S, skewness of distribution of S and I, allow to distinguish copper based alloys, red and dark iron oxides and steel particles. In this work, a method of probabilistic decision-making of class label assignment was proposed, which was based on the analysis of debris-coordinates distribution in the classification plane. The obtained results demonstrated a good availability for the automated wear particle analysis.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.1-9
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• 2017

Innovations from current researches on cloud computing such as applying bio-inspired computing techniques have brought new level solutions in offloading mechanisms. With the growing trend of mobile devices, mobile cloud computing can also benefit from applying bio-inspired techniques. Energy-efficient offloading mechanisms on mobile cloud systems are needed to reduce the total energy consumption but previous works did not consider energy consumption in the decision-making of task distribution. This paper proposes the Particle Swarm Optimization (PSO) as an offloading strategy of cloudlet to data centers where each task is represented as a particle during the process. The collected tasks are classified using K-means clustering on the cloudlet before applying PSO in order to minimize the number of particles and to locate the best data center for a specific task, instead of considering all tasks during the PSO process. Simulation results show that the proposed PSO excels in choosing data centers with respect to energy consumption, while it has accumulated a little more processing time compared to the other approaches.

• Journal of Power Electronics
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• v.16 no.3
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• pp.1097-1109
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• 2016

Fault detection and isolation are related to system monitoring, identifying when a fault has occurred, and determining the type of fault and its location. Fault detection is utilized to determine whether a problem has occurred within a certain channel or area of operation. Fault detection and diagnosis have become increasingly important for many technical processes in the development of safe and efficient advanced systems for supervision. This paper presents an integrated technique for fault diagnosis and classification for open- and short-circuit faults in three-phase inverter circuits. Discrete wavelet transform and principal component analysis are utilized to detect the discontinuity in currents caused by a fault. The features of fault diagnosis are then extracted. A fault dictionary is used to acquire details about transistor faults and the corresponding fault identification. Fault classification is performed with a fuzzy logic system and relevance vector machine (RVM). The proposed model is incorporated with a set of optimization techniques, namely, evolutionary particle swarm optimization (EPSO) and cuckoo search optimization (CSO), to improve fault detection. The combination of optimization techniques with classification techniques is analyzed. Experimental results confirm that the combination of CSO with RVM yields better results than the combinations of CSO with fuzzy logic system, EPSO with RVM, and EPSO with fuzzy logic system.

• Resources Recycling
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• v.31 no.6
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• pp.66-72
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• 2022

The circularity and particle size distribution of products obtained from dry classification after circularity tests using a high-intensity mixer were investigated to evaluate the use of domestic graphite concentrate as a lithium-ion battery material. At a rotor speed of 3,000 rpm, the particle size and circularity of the concentrated sample and product were unchanged. The circularity increased and particle size decreased when the rotor speeds were increased to 6,000 rpm, 10,000 rpm, and 12,000 rpm and the operating time was increased. For instance, the circularity increased from 0.870 to 0.936 when the rotor speed was increased from 3,000 rpm to 12,000 rpm for an operating time of 10 min. After the circularity test, dry classification was performed, wherein the circularity of the coarse particles was found to have increased to 0.947. Round particles were observed in the SEM images, indicating that high circularity was successfully achieved.

• Resources Recycling
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• v.24 no.4
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• pp.56-60
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• 2015

Fine particle classification was performed using products obtained from primary classification process after flotation for efficient application of tailing. The cut size increased with decreasing input pressure from 0.1 MPa to 0.3 MPa and increasing pulp density from 5% to 15% using 2-inch hydrocyclone. The median sizes of overflow and underflow were $6.56{\mu}m$ and $55.45{\mu}m$, respectively at 0.3 MPa with 5% pulp density. The imperfection became closed to ideal separation with increasing the pulp density and decreasing the input pressure. The water content decreased with increasing the pulp density, but the effect of input pressure could be ignored. The water content of underflow was 27.9% at 0.3 MPa with 15% pulp density.

• Journal of Korean Society for Atmospheric Environment
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• v.13 no.5
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• pp.345-353
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• 1997

The purpose of the study was to stastically classify individual PM-10 measured by SEM/EDX (scanning electron microscopy/energy dispersive x-ray analyzer). The SEM/EDX provided various physical parameters like optical diameter, as well as major 18 chemical information (Mg, Al, Si, P, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Br, Pb) for a particle-by-particle basis. The total of 1,419 particles were analyzed for the study. Thus density and mass of each particle can be estimated based on its chemical composition. Further the study developed 4 semisource profiles including highway, oil boiler, incinerator, and soil emissions, where each sample was collected near the source in the ambient air The profiles developed were consisted of mass fractions and their uncertainties based on a particle class concept. To obtain mass fraction of each particle class, an agglomerative hierarchical cluster analysis was initially applied to create particle classes for each sample. Then uncertainties were calculated for each class based on the jacknife method. The 1,258 particles out of 1,419 (88.7%) were assorted in newly generated particle classes. The study provides opportunities to identify particle's source quantitatively and to develope various receptor models.

• Journal of Electrical Engineering and Technology
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• v.7 no.2
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• pp.245-254
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• 2012

In this study, we introduce a prototype-based classifier with feature selection that dwells upon the usage of a biologically inspired optimization technique of Particle Swarm Optimization (PSO). The design comprises two main phases. In the first phase, PSO selects P % of patterns to be treated as prototypes of c classes. During the second phase, the PSO is instrumental in the formation of a core set of features that constitute a collection of the most meaningful and highly discriminative coordinates of the original feature space. The proposed scheme of feature selection is developed in the wrapper mode with the performance evaluated with the aid of the nearest prototype classifier. The study offers a complete algorithmic framework and demonstrates the effectiveness (quality of solution) and efficiency (computing cost) of the approach when applied to a collection of selected data sets. We also include a comparative study which involves the usage of genetic algorithms (GAs). Numerical experiments show that a suitable selection of prototypes and a substantial reduction of the feature space could be accomplished and the classifier formed in this manner becomes characterized by low classification error. In addition, the advantage of the PSO is quantified in detail by running a number of experiments using Machine Learning datasets.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.3
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• pp.443-454
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• 2023

The purpose of this study is to reveal the Korean learners' usage pattern of '의', the genitive particle, according to semantic classification, so that it can be referred to in determining the contents and methods of related education. The method of this study adopts a quantitative analysis using learners corpus established by National Institute of Korean Language. As a result of the analysis, as proficiency increases, the overall frequency of '의' increases and the number of meaning senses used increases. However, the frequency of errors also increases with it. As for the usage pattern of each sense, the meaning of 'ownership, belonging' is the most frequent, and followed by 'acting entity', 'kinship, social relations', and 'relationship(area)'. In conclusion, the meanings of 'acting subjects' and 'relationships(area) need to be supplemented with explicit education. Other meanings need to be discussed, and decisions should be made in consideration of learning purpose and proficiency.