• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.12 no.2
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• pp.69-76
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• 1998

This paper presents a novel algorithm for determining the optimal operation of a cogeneration plant with back-pressure and extraction-condensing turbine/generators. The proposed algorithm determines the optimum load of boilers and turbine/generators, using only one parameter, the steam mass flow rate, which can be obtained directly from on-line measurement during plant operation. The proposed algorithm consists of the non -linear operating cost function, and its correlated constraints. Furthermore, it has been successfully applied to an actual industrial cogeneration plant, with satisfactory results. Comparison of these results with actual operating data has revealed that using the proposed algorithm results in at least 1.2~4.5[%] operating cost saving, depending on the process steam load. Furthermore the proposed algorithm can be easily installed in a process control computer because the required input data can be easily obtained from information available on-line.n-line.

• Bulletin of the Korean Chemical Society
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• v.33 no.7
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• pp.2320-2324
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• 2012

A novel approach for metabolite extraction and fingerprinting was introduced based upon the nano-baskets and emulsion liquid membrane-nuclear magnetic resonance (ELM-NMR) technique. The objective of this method is optimizing the fingerprints, minimizing the metabolic variation from analysis, increasing the likelihood differences, and obtaining the maximum extraction yield. Low molecular weight metabolites in rat serum were recovered by ELMs using 12 nano-baskets of calixarene, as both emulsifier and carrier. The yields of ELMs were optimized by the method of one-at-a-time. According to NMR data, the maximum metabolic variation was achieved using scaffold 4 (4 wt %), n-decane membrane, stirring rate of 300 rpm, treat and phase ratios of 0.3 and 0.8, respectively. The results revealed that some calixarenes tend to extract non-specific macromolecules; and repeatability of fingerprints for 7-mediated ELM was maximum and for 3-mediated ELM was minimum. The yield of extractions was obtained to be higher for n-decane and lower for carbon tetrachloride. Among different membranes, the fingerprints by chlorinated liquid membranes were more repeatable than using toluene or n-decane.

• 한국HCI학회:학술대회논문집
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• 2008.02c
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• pp.172-177
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• 2008

Biometrics is essential for person identification because of its uniqueness from each individuals. Face recognition technology has advantage over other biometrics because of its convenience and non-intrusive characteristics. In this paper, we will present a overview of face recognition technology including face detection, feature extraction, and face recognition system. For face detection, we will describe template based method and face component based approach. PCA and LDA approach will be discussed for feature extraction, and nearest neighbor classifiers -will be covered for matching. Large database and the standardized performance evaluation methodology is essential in order to support state-of-the-art face recognition system. Also, 3D based face recognition technology is the key solution for the pose, lighting and expression variations in many applications.

• The KIPS Transactions:PartD
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• v.17D no.1
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• pp.59-66
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• 2010

Traditional OCR engines are designed to the scanned documents in calibrated environment. Three dimensional perspective distortion and smooth distortion in images are critical problems caused by un-calibrated devices, e.g. image from smart phones. To meet the growing demand of character recognition of texts embedded in the photos acquired from the non-calibrated hand-held devices, we address the problem in three categorical aspects: rotational invariant method of text region extraction, scale invariant method of text line segmentation, and three dimensional perspective mapping. With the integration of the methods, we developed an OCR for camera-captured images.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.241-243
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• 2003

This paper presents an algorithm that automatically extracts buildings among many different features on the earth surface by fusing LIDAR data with panchromatic aerial images. The proposed algorithm consists of three stages such as point level process, polygon level process, parameter space level process. At the first stage, we eliminate gross errors and apply a local maxima filter to detect building candidate points from the raw laser scanning data. After then, a grouping procedure is performed for segmenting raw LIDAR data and the segmented LIDAR data is polygonized by the encasing polygon algorithm developed in the research. At the second stage, we eliminate non-building polygons using several constraints such as area and circularity. At the last stage, all the polygons generated at the second stage are projected onto the aerial stereo images through collinearity condition equations. Finally, we fuse the projected encasing polygons with edges detected by image processing for refining the building segments. The experimental results showed that the RMSEs of building corners in X, Y and Z were ${\pm}$8.1㎝, ${\pm}$24.7㎝, ${\pm}$35.9㎝, respectively.

• Biotechnology and Bioprocess Engineering:BBE
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• v.6 no.6
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• pp.426-432
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• 2001

Perstractive fermentation is a good way to increase the productivity of bioreactors. Us-ing Propionibacteria as the model system, the feasibility of using supported emulsion liquid mem-brane(SELM) fro perstractive fermentation is assessed in this study. Five industrial solvents were considered as the solvent for perparing the SELM. The more polar a solvent, is the higher the par-tition coefficeint However, toxicity of a solvent also increases with its polarity. CO-1055(indus-trial decanol/octanol blend)has the highest partition coefficient toward propionic acid among the solvents that has no molecular toxicity toward Propionibacteria, A preliminary extraction study was conducted using tetradecane as solvent in a hydrophobic hollow fiber contactor. The results confirmed that SELM eliminates the equilibrium limitation of conventional liquid-liquid extrac-tion and allows the use of a non-toxic solvent with low partition coefficient.

• MALSORI
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• no.61
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• pp.63-74
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• 2007

Discrimination of music and speech from the multimedia signal is an important task in audio coding and broadcast monitoring systems. This paper deals with the problem of feature parameter extraction for discrimination of music and speech. The wavelet transform is a multi-resolution analysis method that is useful for analysis of temporal and spectral properties of non-stationary signals such as speech and audio signals. We propose new feature parameters extracted from the wavelet transformed signal for discrimination of music and speech. First, wavelet coefficients are obtained on the frame-by-frame basis. The analysis frame size is set to 20 ms. A parameter $E_{sum}$ is then defined by adding the difference of magnitude between adjacent wavelet coefficients in each scale. The maximum and minimum values of $E_{sum}$ for period of 2 seconds, which corresponds to the discrimination duration, are used as feature parameters for discrimination of music and speech. To evaluate the performance of the proposed feature parameters for music and speech discrimination, the accuracy of music and speech discrimination is measured for various types of music and speech signals. In the experiment every 2-second data is discriminated as music or speech, and about 93% of music and speech segments have been successfully detected.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.3
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• pp.175-184
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• 2008

To construct user profiles automatically, an extraction method for representative keywords from a set of documents is needed. In our previous works, we suggested such a method and showed its usefulness. Here, we apply it to the classification problem and observe how much it contributes to performance improvement. The method can be used as a linear document classifier with few modifications. So, we first evaluate its performance for that case. The method is also applicable to some non-linear classification methods such as GIS (Generalized Instance Set). In GIS algorithm, generalized instances are built from training documents by a generalization function and then the K-NN algorithm is applied to them, where the method can be used as a generalization function. For comparative works, two famous linear classification methods, Rocchio and Widrow-Hoff algorithms, are also used. Experimental results show that our method is better than the others for the case that only positive documents are considered, but not when negative documents are considered together.

• Smart Structures and Systems
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• v.25 no.3
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• pp.265-277
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• 2020

Recently, the concept of "drive-by" bridge monitoring system using indirect measurements from a passing vehicle to extract key parameters of a bridge has been rapidly developed. As one of the most key parameters of a bridge, the natural frequency has been successfully extracted theoretically and in practice using indirect measurements. The frequency of bridge is generally calculated applying Fast Fourier Transform (FFT) directly. However, it has been demonstrated that with the increase in vehicle velocity, the estimated frequency resolution of FFT will be very low causing a great extracted error. Moreover, because of the low frequency resolution, it is hard to detect the frequency drop caused by any damages or degradation of the bridge structural integrity. This paper will introduce a new technique of bridge frequency extraction based on Hilbert Transform (HT) that is not restricted to frequency resolution and can, therefore, improve identification accuracy. In this paper, deriving from the vehicle response, the closed-form solution associated with bridge frequency removing the effect of vehicle velocity is discussed in the analytical study. Then a numerical Vehicle-Bridge Interaction (VBI) model with a quarter car model is adopted to demonstrate the proposed approach. Finally, factors that affect the proposed approach are studied, including vehicle velocity, signal noise, and road roughness profile.

• Journal of Integrative Natural Science
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• v.11 no.4
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• pp.167-183
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• 2018

Electroencephalogram (EEG) recording provides a new way to support human-machine communication. It gives us an opportunity to analyze the neuro-dynamics of human cognition. Machine learning is a powerful for the EEG classification. In addition, machine learning can compensate for high variability of EEG when analyzing data in real time. However, the optimal EEG electrode location must be prioritized in order to extract the most relevant features from brain wave data. In this paper, we propose an intelligent system model for the extraction of EEG data by training the optimal electrode location of EEG in a specific problem. The proposed system is basically a fuzzy system and uses a neural network structurally. The fuzzy clustering method is used to determine the optimal number of fuzzy rules using the features extracted from the EEG data. The parameters and weight values found in the process of determining the number of rules determined here must be tuned for optimization in the learning process. Genetic algorithms are used to obtain optimized parameters. We present useful results by using optimal rule numbers and non - symmetric membership function using EEG data for four movements with the right arm through various experiments.