• Journal of Power Electronics
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• v.16 no.5
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• pp.1904-1917
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• 2016

The moving average filter (MAF) is widely utilized to improve the disturbance rejection capability of phase-locked loops (PLLs). This is of vital significance for the grid-integration and stable operation of power electronic converters to electric power systems. However, the open-loop bandwidth is drastically reduced after incorporating a MAF into the PLL structure, which makes the dynamic response sluggish. To overcome this shortcoming, some new techniques have recently been proposed to improve the transient response of MAF-based PLLs. In this paper, a comprehensive performance comparison of advanced MAF-based PLL algorithms is presented. This comparison includes HPLL, MPLC-PLL, QT1-PLL, and DMAF-PLL. Various disturbances, such as grid voltage sag, voltage flicker, harmonics distortion, phase-angle and frequency jumps, DC offsets and noise, are considered to experimentally test the dynamic performances of these PLL algorithms. Finally, an improved positive sequence extraction method for a HPLL under the frequency jumps scenario is presented to compensate for the steady-state error caused by non-frequency adaptive DSC, and a satisfactory performance has been achieved.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.1
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• pp.370-381
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• 2012

Multi-criteria result extraction is crucial in many scientific applications that support real-time stream processing, such as habitat research and disaster monitoring. Skyline evaluation is computational intensive especially over continuous time-interval data streams where each object has its own customized expiration time. In this work, we propose TI-Sky - a continuous skyline evaluation framework. To ensure correctness, the result space needs to be continuously maintained as new objects arrive and older objects expire. TI-Sky strikes a perfect balance between the costs of continuously maintaining the result space and the costs of computing the final skyline result from this space whenever a pull-based user query is received. Our key principle is to incrementally maintain a partially precomputed skyline result space - however doing so efficiently by working at a higher level of abstraction. TI-Sky's algorithms for insertion, deletion, purging and result retrieval exploit both layers of granularity. Our experimental study demonstrates the superiority of TI-Sky over existing techniques to handle a wide variety of data sets.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.3
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• pp.297-303
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• 2009

In this paper, we propose the biomedical spectral pattern classification techniques by the fusion scheme based on the SpPCA and MLP in extended feature space. A conventional PCA technique for the dimension reduction has the problem that it can't find an optimal transformation matrix if the property of input data is nonlinear. To overcome this drawback, we extract features by the SpPCA technique in extended space which use the local patterns rather than whole patterns. In the classification step, individual classifier based on MLP calculates the similarity of each class for local features. Finally, biomedical spectral patterns is classified by the fusion scheme to effectively combine the individual information. As the simulation results to verify the effectiveness, the proposed method showed more improved classification results than conventional methods.

• Journal of Biosystems Engineering
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• v.39 no.3
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• pp.235-243
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• 2014

Purpose: The aim of this study was to prepare and evaluate a natural material extracted from germinated brown rice (GBR). Herein, we evaluated whether the natural material could positively activate the biological effects seen during bone formation, including enhancement of metabolic activity, osteogenesis, and the expression of vascular endothelial growth factor (VEGF), one of the growth factors in human osteoblast-like cells. Methods: The natural material was created by a hot water extraction process after being soaked for 2~3 days in tap water and dried at $50^{\circ}C$. The material was characterized using field emission scanning electron microscopy (FE-SEM), energy dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD), and Fourier transformed infrared (FTIR) spectroscopy. The biological behaviors of the material were also investigated; we performed tests to assess cell cytotoxicity, metabolic activity, osteogenic markers related to bone formation, and VEGF. Results: The EDX, XRD, and FTIR results for the natural material indicated the presence of organic compounds. The natural material caused positive increases in cell metabolic activity and mineralized bone formation without cytotoxicity. The protein levels in the extract for the $6.25{\mu}g/mL$, $12.25{\mu}g/mL$, $25{\mu}g/mL$, $50{\mu}g/mL$, and $100{\mu}g/mL$ groups were significantly different from that for the control. Conclusions: The GBR-based natural material was easy to prepare and had characteristics of a potential biomaterial. The biocompatibility of this natural material was evaluated using in vitro techniques; our findings indicate that this novel material is promising for agricultural and biological applications.

• Analytical Science and Technology
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• v.25 no.1
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• pp.33-38
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• 2012

Despite the importance of chloride binding, it is very difficult to measure the binding capacity, in particular, for the concrete body in an existing structure: in fact, the measurement procedure for chloride binding is much influenced by the environmental condition such as temperature, fineness of sample and pore water extraction techniques. The present study concerns the quantification of the binding capacity of chloride ions in concrete using the X-ray diffraction (XRD) technique. Once the binding isotherm of chlorides was determined by the Langmuir isotherm, as a function of the W/C, curing age and binder type, the generation of bound chlorides (i.e. Friedel's salt) was simultaneously ensured by the XRD technique. The amount of bound chloride was then determined by analyzing the peak intensity for the bound chlorides in the XRD curve. It was found that an increase in the curing age and a decrease in the W/C resulted in an increase in the binding capacity.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.20 no.11
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• pp.1009-1017
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• 2010

As rotating machines play an important role in industrial applications such as aeronautical, naval and automotive industries, many researchers have developed various condition monitoring system and fault diagnosis system by applying various techniques such as signal processing and pattern recognition. Recently, fault diagnosis systems using artificial neural network have been proposed. For effective fault diagnosis, this paper used MLP(multi-layer perceptron) network which is widely used in pattern classification. Since using obtained signals without preprocessing as inputs of neural network can decrease performance of fault classification, it is very important to extract significant features of captured signals and to apply suitable features into diagnosis system according to the kinds of obtained signals. Therefore, this paper proposes the decision method of the proper feature vectors about each fault signal for neural-network-based fault diagnosis system. We applied LPC coefficients, maximum magnitudes of each spectral section in FFT and RMS(root mean square) and variance of wavelet coefficients as feature vectors and selected appropriate feature vectors as comparing error ratios of fault diagnosis for sound, vibration and current fault signals. From experiment results, LPC coefficients and maximum magnitudes of each spectral section showed 100 % diagnosis ratios for each fault and the method using wavelet coefficients had noise-robust characteristic.

• Korean Journal of Food Science and Technology
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• v.13 no.1
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• pp.20-24
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• 1981

Residues of neoasozine in rice grain were determined by neutron activation and colorimetric techniques. Twice application of the chemical before flowering did not lead to any increased residue level while 4-times application resulted in significant increase in the residue level up to 0.54-0.75 mg $As_2O_3/kg$. The partition ratio of arsenic residues into polished rice grain and bran was 73 : 27 in 100% polishing while most of the residues in the bran was transferred to oil cake fraction during solvent extraction, reaching up to 2.9 mg $As_2O_3/kg$. The neutron activation technique was advantageous because of its high sensitivity and the smaller sample amounts required for analysis.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.24 no.2
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• pp.217-226
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• 2006

Extraction and tracking objects are fundamental and important steps of the digital image processing and computer vision. Many algorithms about extracting and tracking objects have been developed. In this research, a method is suggested for tracking a moving object using a pair of CCD cameras and calculating the coordinate of the moving object. A 1/100 miniature of soccer field was made to apply the developed algorithms. After candidates were selected from the acquired images using the RGB value of a moving object (soccer ball), the object was extracted using its size (MBR size) among the candidates. And then, image coordinates of a moving object are obtained. The real-time position of a moving object is tracked in the boundary of the expected motion, which is determined by centering the moving object. The 3D position of a moving object can be obtained by conducting the relative orientation, absolute orientation, and space intersection of a pair of the CCD camera image.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.3
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• pp.205-216
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• 2014

Multi-camera systems have been widely used as cost-effective tools for the collection of geospatial data for various applications. In order to fully achieve the potential accuracy of these systems for object space reconstruction, careful system calibration should be carried out prior to data collection. Since the structural integrity of the involved cameras' components and system mounting parameters cannot be guaranteed over time, multi-camera system should be frequently calibrated to confirm the stability of the estimated parameters. Therefore, automated techniques are needed to facilitate and speed up the system calibration procedure. The automation of the multi-camera system calibration approach, which was proposed in the first part of this paper, is contingent on the automated detection, localization, and identification of the object space signalized targets in the images. In this paper, the automation of the proposed camera calibration procedure through automatic target extraction and labelling approaches will be presented. The introduced automated system calibration procedure is then implemented for a newly-developed multi-camera system while considering the optimum configuration for the data collection. Experimental results from the implemented system calibration procedure are finally presented to verify the feasibility the proposed automated procedure. Qualitative and quantitative evaluation of the estimated system calibration parameters from two-calibration sessions is also presented to confirm the stability of the cameras' interior orientation and system mounting parameters.

• Journal of Information Technology Services
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• v.16 no.3
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• pp.167-183
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• 2017

The rapid growth of information technology and mobile service platforms, i.e., internet, google, and facebook, etc. has led the abundance of data. Due to this environment, the world is now facing a revolution in the process that data is searched, collected, stored, and shared. Abundance of data gives us several opportunities to knowledge discovery and data mining techniques. In recent years, data mining methods as a solution to discovery and extraction of available knowledge in database has been more popular in e-commerce service fields such as, in particular, movie recommendation. However, most of the classification approaches for predicting the movie popularity have used only several types of information of the movie such as actor, director, rating score, language and countries etc. In this study, we propose a classification-based support vector machine (SVM) model for predicting the movie popularity based on movie's genre data and social network data. Social network analysis (SNA) is used for improving the classification accuracy. This study builds the movies' network (one mode network) based on initial data which is a two mode network as user-to-movie network. For the proposed method we computed degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality as centrality measures in movie's network. Those four centrality values and movies' genre data were used to classify the movie popularity in this study. The logistic regression, neural network, $na{\ddot{i}}ve$ Bayes classifier, and decision tree as benchmarking models for movie popularity classification were also used for comparison with the performance of our proposed model. To assess the classifier's performance accuracy this study used MovieLens data as an open database. Our empirical results indicate that our proposed model with movie's genre and centrality data has by approximately 0% higher accuracy than other classification models with only movie's genre data. The implications of our results show that our proposed model can be used for improving movie popularity classification accuracy.