• 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.

• Journal of Biosystems Engineering
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• v.35 no.2
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• pp.138-145
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• 2010

An on-line algorithm that discriminates brown rice kernels on their echelon feeder using color image processing is presented for quality inspection. A rapid color image segmentation algorithm based on Bayesian clustering method was developed by means of the look-up table which was made from the significant clusters selected by experts. A robust estimation method was presented to improve the stability of color clusters. Discriminant analysis of color distributions was employed to distinguish nine types of brown rice kernels. Discrimination accuracies of the on-line discrimination algorithm were ranged from 72% to 85% for the sound, cracked, green-transparent and green-opaque, greater than 93% for colored, red, and unhulled, about 92% for white-opaque and 67% for chalky, respectively.

• YAKHAK HOEJI
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• v.53 no.2
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• pp.51-59
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• 2009

Quantitative analysis of (+)-catechin (C), (-)-epigallocatechin (EGC), (-)-epicatechin (EC), (-)-epigallocatechin gallate (EGCG), (-)-epicatechin gallate (ECG) and caffeine in commercial green tea was carried out by HPLC employing gradient elution of 0.1% acetic acid and acetonitrile on ODS column. The optimized HPLC method provided satisfactory linearity, accuracy and precision. The relationship between the concentration of the components and cultivated region of the commercial green tea was not significant, while the concentration of EGCG, ECG and caffeine decreased significantly in the later harvested green tea samples (p<0.01). Multivariate analysis of the components was performed in order to characterize and evaluate the cultivated region and harvest period-related variation. Hierarchical clustering and discriminant analysis were applied to classify the geographical and seasonal origins of the green tea samples. The classification accuracy of the cultivated region and harvest period by discriminant analysis was 95% and 91%, respectively, indicating that this method could be reliable and convenient for the quality control of herbal products with different origin.

• Electronics and Telecommunications Trends
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• v.37 no.4
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• pp.1-10
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• 2022

As the response to climate change becomes a more pressing global issue, so do expectations for climate change in the green information and communication technology (ICT) industry and the possibility of solving environmental problems through ICT. However, because the green ICT industry is still in its early stages, there is little research on it. Understanding the startup ecosystem in the industry is helpful for recognizing innovation trends in emerging technologies such as green ICT. In this regard, this paper investigates the current state and characteristics of the green ICT ecosystem and presents implications based on an examination of startup venture capital investment trends and submarket identification in the green ICT industry as emphasized by the carbon neutrality paradigm shift. The analysis included 4,807 companies and 3,990 funding records, as well as exploratory data analysis and "k-means" clustering techniques.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.2
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• pp.108-112
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• 2012

A sustainable production of electricity is essential for low carbon green growth in South Korea. The generation of wind power as renewable energy has been rapidly growing around the world. Undoubtedly wind energy is unlimited in potential. However, due to its own intermittency and volatility, there are difficulties in the effective harvesting of wind energy and the integration of wind power into the current electric power grid. To cope with this, many works have been done for wind speed and power forecasting. It is reported that, compared with physical persistent models, statistical techniques and computational methods are more useful for short-term forecasting of wind power. Among them, support vector regression (SVR) has much attention in the literature. This paper proposes an SVR based wind speed forecasting. To improve the forecasting accuracy, a fuzzy clustering is adopted in the process of SVR modeling. An illustrative example is also given by using real-world wind farm dataset. According to the experimental results, it is shown that the proposed method provides better forecasts of wind power.

• Transportation Technology and Policy
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• v.10 no.1
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• pp.18-26
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• 2013

• Journal of Korea Multimedia Society
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• v.19 no.1
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• pp.68-79
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• 2016

Electromyographic (EMG) signals have been widely used as motion commands of prosthetic arms. Although EMG signals contain meaningful information including the movement intentions of human body, it is difficult to predict the subject's motion by analyzing EMG signals in real-time due to the difficulties in extracting motion information from the signals including a lot of noises inherently. In this paper, four Ag/AgCl electrodes are placed on the surface of the subject's major muscles which are in charge of four upper arm movements (wrist flexion, wrist extension, ulnar deviation, finger flexion) to measure EMG signals corresponding to the movements. The measured signals are sampled using DAQ module and clustered sequentially. The Fuzzy C-Means (FCMs) method calculates the center values of the clustered data group. The fuzzy system designed to detect the upper arm movement intention utilizing the center values as input signals shows about 90% success in classifying the movement intentions.

• Journal of the Korean Chemical Society
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• v.63 no.2
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• pp.78-84
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• 2019

High-resolution $^1H$ NMR spectroscopic technique has been widely used as one of the most powerful analytical tools in food chemistry as well as to define molecular structure. The $^1H$ NMR spectra-based metabolomics has focused on classification and chemometric analysis of complex mixtures. The principal component analysis (PCA), an unsupervised clustering method and used to reduce the dimensionality of multivariate data, facilitates direct peak quantitation and pattern recognition. Using a combination of these techniques, the various green teas and black teas brewed were investigated via metabolite profiling. These teas were characterized based on the leaf size and country of cultivation, respectively.

• Animal cells and systems
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• v.5 no.3
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• pp.253-262
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• 2001

Ligand-receptor clustering event is the most important step in leukocyte adhesion and spreading on endothelial cells. Intercellular adhesion molecule-1 (ICAM-1) has been shown to enhance leukocyte adhesion, but the molecular event during the process of adhesion is unclear. To visualize the dynamics of ICAM-1 movement during adhesion, we have engineered stable Chinese hamster ovary cell lines expressing ICAM-1 fused to a green fluorescent protein (IC1_GFP/CHO) and examined them under the fluorescence microscopy. The transfection of IC1_GFP alone in these cells was sufficient to support the adhesion of K562 cells that express $\alpha$L$\beta$2 (LFA-1) integrin stimulated by CBR LFA-1/2 mAb. This phenomenon was mediated by ICAM-1-LFA-1 interactions, as an mAb that specifically inhibits ICAM-1-LFA-1 interaction (RRl/l) completely abolished the adhesion of LFA-1＊ cells to IC1_ GFP/CHO cells. We found that the characteristic of adhesion was followed almost immediately (~10 min) by the rapid accumulation of ICAM-1 on CHO cells at a tight interface between the two cells. Interestingly, ICI_GFP/CHO cells projected plasma membrane and encircled approximately half surface of LFA-1+ cells, as defined by confocal microscopy. This unusual phenomenon was also confirmed on HUVEC after adhesion of LFA-1＊ cells. Neither cytochalasin D nor 2,3-butanedione 2-monoxime an inhibitor of myosin light chain kinase blocked LFA-1-mediated ICAM-1 clustering, indicating that actin cytoskeleton and myosin-dependent contractility are not necessary for ICAM-1 clustering. Taken together, we suggest that leukocyte adhesion to endothelial cells induces specialized form of ICAM-1 clustering that is distinct from immunological synapse mediated by T cell interaction with antigen presenting cells.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.31 no.4
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• pp.255-260
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• 2018

In recent years, increasing electricity use has led to considerable interest in green energy. In order to effectively supply, cut off, and operate an electric power system, many electric power facilities such as gas insulation switch (GIS), cable, and large substation facilities with higher densities are being developed to meet demand. However, because of the increased use of aging electric power facilities, safety problems are emerging. Electromagnetic wave and leakage current detection are mainly used as sensing methods to detect live-line partial discharges. Although electromagnetic sensors are excellent at providing an initial diagnosis and very reliable, it is difficult to precisely determine the fault point, while leakage current sensors require a connection to the ground line and are very vulnerable to line noise. The partial discharge characteristic in particular is accompanied by statistical irregularity, and it has been reported that proper statistical processing of data is very important. Therefore, in this paper, we present the results of analyzing ${\Phi}-q-n$ cluster distributions of partial discharge characteristics by using K-means clustering to develop an expert partial discharge diagnosis system generated in a GIS facility.